A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

[1]  Nei Kato,et al.  Characterizing Flow, Application, and User Behavior in Mobile Networks: A Framework for Mobile Big Data , 2018, IEEE Wireless Communications.

[2]  Jun Guo,et al.  Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Timothy L. Brown,et al.  Speech-Based Interaction with In-Vehicle Computers: The Effect of Speech-Based E-Mail on Drivers' Attention to the Roadway , 2001, Hum. Factors.

[4]  Chris Mellish,et al.  Advances in Instance Selection for Instance-Based Learning Algorithms , 2002, Data Mining and Knowledge Discovery.

[5]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Gerhard Fettweis,et al.  5G: Personal mobile internet beyond what cellular did to telephony , 2014, IEEE Communications Magazine.

[7]  Geoffrey E. Hinton,et al.  Discovering Binary Codes for Documents by Learning Deep Generative Models , 2011, Top. Cogn. Sci..

[8]  Yael Ben-Haim,et al.  A Streaming Parallel Decision Tree Algorithm , 2010, J. Mach. Learn. Res..

[9]  Tamara G. Kolda,et al.  Scalable Tensor Decompositions for Multi-aspect Data Mining , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[10]  Klaus-Robert Müller,et al.  Incremental Support Vector Learning: Analysis, Implementation and Applications , 2006, J. Mach. Learn. Res..

[11]  José Francisco Martínez Trinidad,et al.  Building fast decision trees from large training sets , 2012, Intell. Data Anal..

[12]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[13]  Yun Lei,et al.  Advances in deep neural network approaches to speaker recognition , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Michael I. Jordan Divide-and-conquer and statistical inference for big data , 2012, KDD.

[15]  Victor Ciesielski,et al.  Anomaly Detection Using Replicator Neural Networks Trained on Examples of One Class , 2014, SEAL.

[16]  Ning Lu,et al.  Soft-defined heterogeneous vehicular network: architecture and challenges , 2015, IEEE Network.

[17]  Kai Sze Hong,et al.  Speech recognition interactive system for vehicle , 2017, 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA).

[18]  Yuanyuan Qiao,et al.  Mobile big-data-driven rating framework: measuring the relationship between human mobility and app usage behavior , 2016, IEEE Network.

[19]  Quoc V. Le,et al.  Measuring Invariances in Deep Networks , 2009, NIPS.

[20]  Yang Liu,et al.  Simultaneous Bayesian Sparse Approximation With Structured Sparse Models , 2016, IEEE Transactions on Signal Processing.

[21]  Yanqiu Chen,et al.  User interest acquisition by adding home and work related contexts on mobile big data analysis , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[22]  Cees T. A. M. de Laat,et al.  Addressing big data issues in Scientific Data Infrastructure , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[23]  Zhenglei Yi,et al.  Social Computing for Mobile Big Data , 2016, Computer.

[24]  Li-Rong Dai,et al.  A Regression Approach to Speech Enhancement Based on Deep Neural Networks , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[25]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[26]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[27]  Charu C. Aggarwal,et al.  Outlier Analysis , 2013, Springer New York.

[28]  Ahmad Salman,et al.  Learning Speaker-Specific Characteristics With a Deep Neural Architecture , 2011, IEEE Transactions on Neural Networks.

[29]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[30]  Hao Luo,et al.  A Cross-Domain Recommendation Model for Cyber-Physical Systems , 2013, IEEE Transactions on Emerging Topics in Computing.

[31]  U. Fayyad On the induction of decision trees for multiple concept learning , 1991 .

[32]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[33]  Ilyas Alper Karatepe,et al.  Big data caching for networking: moving from cloud to edge , 2016, IEEE Communications Magazine.

[34]  Ilmir Z. Ibragimov,et al.  Comparison of ROS-based visual SLAM methods in homogeneous indoor environment , 2017, 2017 14th Workshop on Positioning, Navigation and Communications (WPNC).

[35]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[36]  Nei Kato,et al.  A Mobility Analytical Framework for Big Mobile Data in Densely Populated Area , 2017, IEEE Transactions on Vehicular Technology.

[37]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[38]  James Martens,et al.  Deep learning via Hessian-free optimization , 2010, ICML.

[39]  Yan Huang,et al.  Management and application of mobile big data , 2015, Int. J. Embed. Syst..

[40]  Yun Lei,et al.  Application of convolutional neural networks to speaker recognition in noisy conditions , 2014, INTERSPEECH.

[41]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[42]  Aaron Striegel,et al.  Analyzing the longitudinal impact of proximity, location, and personality on smartphone usage , 2014 .

[43]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[44]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[45]  Yu Zhang,et al.  An Algorithm for Analyzing the City Residents' Activity Information through Mobile Big Data Mining , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[46]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[47]  DeLiang Wang,et al.  Deep neural network based spectral feature mapping for robust speech recognition , 2015, INTERSPEECH.

[48]  Lin Zhang,et al.  Geo-cascading and community-cascading in social networks: Comparative analysis and its implications to edge caching , 2018, Inf. Sci..

[49]  Yusuke Shinohara,et al.  Adversarial Multi-Task Learning of Deep Neural Networks for Robust Speech Recognition , 2016, INTERSPEECH.

[50]  Simon Fong,et al.  Incrementally optimized decision tree for noisy big data , 2012, BigMine '12.

[51]  Fabrizio Angiulli,et al.  Distributed Nearest Neighbor-Based Condensation of Very Large Data Sets , 2007, IEEE Transactions on Knowledge and Data Engineering.

[52]  Zhi-Hua Zhou,et al.  Learning to hash for big data: Current status and future trends , 2015 .

[53]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[54]  Carey L. Williamson,et al.  Characterizing and modeling user mobility in a cellular data network , 2005, PE-WASUN '05.

[55]  Mario Gerla,et al.  Vehicular networks and the future of the mobile internet , 2011, Comput. Networks.

[56]  Qie Sun,et al.  Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems , 2014 .

[57]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[58]  Honggang Zhang,et al.  Variational Bayesian Matrix Factorization for Bounded Support Data , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  Yuanyuan Qiao,et al.  Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest , 2017, IEEE Transactions on Vehicular Technology.

[60]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[61]  Jun Liu,et al.  User intention understanding from scratch , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).

[62]  Lin Ma,et al.  An analysis of supply chain restructuring based on Big Data and mobile Internet —A case study of warehouse-type supermarkets , 2015, 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS).

[63]  Miguel R. D. Rodrigues,et al.  Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach , 2013, IEEE Transactions on Signal Processing.

[64]  Javad Rahimipour Anaraki,et al.  Improving fuzzy-rough quick reduct for feature selection , 2011, 2011 19th Iranian Conference on Electrical Engineering.

[65]  Brian Kingsbury,et al.  New types of deep neural network learning for speech recognition and related applications: an overview , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[66]  Gerald Penn,et al.  Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[67]  R. Fergus,et al.  Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[68]  Yu Zhang,et al.  Clustering Analysis in the Wireless Propagation Channel with a Variational Gaussian Mixture Model , 2020, IEEE Transactions on Big Data.

[69]  Zhen Yang,et al.  Decorrelation of Neutral Vector Variables: Theory and Applications , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[70]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[71]  Chong Wang,et al.  Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.

[72]  Margaret Martonosi,et al.  Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.

[73]  Jun Guo,et al.  The Role of Data Analysis in the Development of Intelligent Energy Networks , 2017, IEEE Network.

[74]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[75]  Lei Zhang,et al.  Robust Online Matrix Factorization for Dynamic Background Subtraction , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[76]  Seref Sagiroglu,et al.  Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[77]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[78]  Markus Flierl,et al.  Bayesian estimation of Dirichlet mixture model with variational inference , 2014, Pattern Recognit..

[79]  Samy Bengio,et al.  Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks , 1999, NIPS.

[80]  Shangguang Wang,et al.  An overview of Internet of Vehicles , 2014, China Communications.

[81]  Cecilia Mascolo,et al.  Keep Your Friends Close and Your Facebook Friends Closer: A Multiplex Network Approach to the Analysis of Offline and Online Social Ties , 2014, ICWSM.

[82]  Zhengrong Liang,et al.  Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism , 2017, IEEE Transactions on Medical Imaging.

[83]  Fabrizio Angiulli,et al.  Fast Nearest Neighbor Condensation for Large Data Sets Classification , 2007, IEEE Transactions on Knowledge and Data Engineering.

[84]  Michael R. Lyu,et al.  Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally , 2008, IEEE Transactions on Neural Networks.

[85]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[86]  Derek C. Rose,et al.  Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[87]  Seiichi Nakagawa,et al.  Robust speech recognition using DNN-HMM acoustic model combining noise-aware training with spectral subtraction , 2015, INTERSPEECH.

[88]  DeLiang Wang,et al.  Ideal ratio mask estimation using deep neural networks for robust speech recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[89]  Khe Chai Sim,et al.  Improving robustness of deep neural networks via spectral masking for automatic speech recognition , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[90]  Aleksandar Kuzmanovic,et al.  Measuring serendipity: connecting people, locations and interests in a mobile 3G network , 2009, IMC '09.

[91]  G. Gates The Reduced Nearest Neighbor Rule , 1998 .

[92]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[93]  Taghi M. Khoshgoftaar,et al.  A survey of open source tools for machine learning with big data in the Hadoop ecosystem , 2015, Journal of Big Data.

[94]  Zhanyu Ma,et al.  Sentiment Analysis by Exploring Large Scale Web-based Chinese Short Text , 2018 .

[95]  Biao Song,et al.  Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities , 2017, IEEE Access.

[96]  Fang Liu,et al.  Characterizing User Behavior in Mobile Internet , 2015, IEEE Transactions on Emerging Topics in Computing.

[97]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

[98]  Zhanyu Ma,et al.  Text-Independent Speaker Identification Using the Histogram Transform Model , 2016, IEEE Access.

[99]  Xue-wen Chen,et al.  Large-Scale Deep Belief Nets With MapReduce , 2014, IEEE Access.

[100]  Jianhua Zhang,et al.  Data scheme-based wireless channel modeling method: motivation, principle and performance , 2017, Journal of Communications and Information Networks.

[101]  Arne Leijon,et al.  Bayesian Estimation of Beta Mixture Models with Variational Inference , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[102]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[103]  Longbiao Wang,et al.  Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification , 2015, EURASIP J. Audio Speech Music. Process..

[104]  Mohsen Guizani,et al.  5G wireless backhaul networks: challenges and research advances , 2014, IEEE Network.

[105]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[106]  Dong Yu,et al.  Conversational Speech Transcription Using Context-Dependent Deep Neural Networks , 2012, ICML.

[107]  Rong Jin,et al.  Online feature selection for mining big data , 2012, BigMine '12.

[108]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[109]  Cecilia Mascolo,et al.  A multilayer approach to multiplexity and link prediction in online geo-social networks , 2016, EPJ Data Science.

[110]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[111]  Nicholas D. Lane,et al.  Can Deep Learning Revolutionize Mobile Sensing? , 2015, HotMobile.

[112]  Xiaoming Xi,et al.  Mining Sequential Update Summarization with Hierarchical Text Analysis , 2016, Mob. Inf. Syst..

[113]  George Papandreou,et al.  Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[114]  Graham W. Taylor,et al.  Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.

[115]  Shonali Krishnaswamy,et al.  Mobile Big Data Analytics: Research, Practice, and Opportunities , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[116]  Marc'Aurelio Ranzato,et al.  Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.

[117]  W. Bastiaan Kleijn,et al.  Function Splitting and Quadratic Approximation of the Primal-Dual Method of Multipliers for Distributed Optimization Over Graphs , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[118]  Yanmin Qian,et al.  Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[119]  Jesper Jensen,et al.  MMSE based noise PSD tracking with low complexity , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[120]  Jianhua Zhang,et al.  The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model , 2016, China Communications.

[121]  Jun Guo,et al.  Histogram transform model using MFCC features for text-independent speaker identification , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[122]  Jörg Meyer,et al.  Multi-channel speech enhancement in a car environment using Wiener filtering and spectral subtraction , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[123]  Q. Henry Wu,et al.  Online training of support vector classifier , 2003, Pattern Recognit..

[124]  Jun Guo,et al.  DNN Filter Bank Cepstral Coefficients for Spoofing Detection , 2017, IEEE Access.

[125]  Zongben Xu,et al.  Universal Approximation of Extreme Learning Machine With Adaptive Growth of Hidden Nodes , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[126]  Richard Heusdens,et al.  Distributed Optimization Using the Primal-Dual Method of Multipliers , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[127]  Jun Guo,et al.  Effect of multi-condition training and speech enhancement methods on spoofing detection , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).

[128]  Houbing Song,et al.  Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications , 2016, IEEE Access.

[129]  G. Gates,et al.  The reduced nearest neighbor rule (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[130]  Rajat Raina,et al.  Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.

[131]  Chunyun Zhang,et al.  Mining activation force defined dependency patterns for relation extraction , 2015, Knowl. Based Syst..

[132]  Chunyun Zhang,et al.  Construction of semantic bootstrapping models for relation extraction , 2015, Knowl. Based Syst..

[133]  Vitaly Klyuev,et al.  SVR-based outlier detection and its application to hotel ranking , 2014, 2014 IEEE 6th International Conference on Awareness Science and Technology (iCAST).

[134]  Yaonan Wang,et al.  Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[135]  Richard Heusdens,et al.  Large Scale LP Decoding with Low Complexity , 2013, IEEE Communications Letters.

[136]  Johannes Schöning,et al.  Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage , 2011, Mobile HCI.

[137]  Katarzyna Musial,et al.  Multidimensional Social Network in the Social Recommender System , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[138]  Zhanyu Ma,et al.  Adversarial Network Bottleneck Features for Noise Robust Speaker Verification , 2017, INTERSPEECH.

[139]  Richard Heusdens,et al.  Linear Coordinate-Descent Message Passing for Quadratic Optimization , 2012, Neural Computation.

[140]  Rin-ichiro Taniguchi,et al.  Fast Feature Extraction Approach for Multi-Dimension Feature Space Problems , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[141]  K. Senthamarai Kannan,et al.  Multiple Linear Regression Models in Outlier Detection , 2012 .

[142]  Dong Yu,et al.  Scalable stacking and learning for building deep architectures , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[143]  Luca Maria Gambardella,et al.  Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition , 2010, ArXiv.

[144]  John H. L. Hansen,et al.  Robust i-vector extraction for neural network adaptation in noisy environment , 2015, INTERSPEECH.

[145]  Li Yunzhou,et al.  A learning-based channel model for synergetic transmission technology , 2015, China Communications.

[146]  Ch. Ramesh Babu,et al.  Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds , 2016 .

[147]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

[148]  Shoudong Huang,et al.  Robust Incremental SLAM Under Constrained Optimization Formulation , 2018, IEEE Robotics and Automation Letters.

[149]  Lin Zhang,et al.  Learning Geographical and Mobility Factors for Mobile Application Recommendation , 2017, IEEE Intelligent Systems.

[150]  Leslie S. Smith,et al.  Feature subset selection in large dimensionality domains , 2010, Pattern Recognit..

[151]  Hwee Pink Tan,et al.  Mobile big data analytics using deep learning and apache spark , 2016, IEEE Network.

[152]  Narasimhan Sundararajan,et al.  Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[153]  Heiga Zen,et al.  Statistical parametric speech synthesis using deep neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[154]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[155]  Yuanyuan Qiao,et al.  User location prediction with energy efficiency model in the Long Term‐Evolution network , 2016, Int. J. Commun. Syst..

[156]  Marco Conti,et al.  The structure of online social networks mirrors those in the offline world , 2015, Soc. Networks.

[157]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[158]  Jun Guo,et al.  Dirichlet mixture modeling to estimate an empirical lower bound for LSF quantization , 2014, Signal Process..

[159]  Saint John Walker Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2014 .

[160]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[161]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[162]  Dong Yu,et al.  Tensor Deep Stacking Networks , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[163]  Graham J. Williams,et al.  Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum] , 2014, IEEE Computational Intelligence Magazine.

[164]  Bhuvana Ramabhadran,et al.  Invariant Representations for Noisy Speech Recognition , 2016, ArXiv.

[165]  Yuhua Li,et al.  Selecting Critical Patterns Based on Local Geometrical and Statistical Information , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[166]  Jianhua Zhang,et al.  Clustering in wireless propagation channel with a statistics-based framework , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[167]  Tong Wang,et al.  Big data enabled user behavior characteristics in mobile internet , 2017, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP).

[168]  Lo Yang,et al.  Dissimilarity data in statistical model building and machine learning , 2012 .

[169]  ZHANGJianHua Review of wideband MIMO channel measurement and modeling for IMT-Advanced systems , 2012 .

[170]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[171]  Cecilia Mascolo,et al.  Measuring Urban Social Diversity Using Interconnected Geo-Social Networks , 2016, WWW.

[172]  Han Wang,et al.  Ensemble Based Extreme Learning Machine , 2010, IEEE Signal Processing Letters.

[173]  Bing Wu,et al.  A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications , 2016, IEEE Access.

[174]  Abdulmotaleb El-Saddik,et al.  Toward Social Internet of Vehicles: Concept, Architecture, and Applications , 2015, IEEE Access.

[175]  Yu Zhang,et al.  A PCA-based modeling method for wireless MIMO channel , 2017, 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[176]  Fuzhen Zhuang,et al.  Parallel extreme learning machine for regression based on MapReduce , 2013, Neurocomputing.

[177]  Jun Du,et al.  An Experimental Study on Speech Enhancement Based on Deep Neural Networks , 2014, IEEE Signal Processing Letters.

[178]  Jen-Tzung Chien,et al.  Deep neural network driven mixture of PLDA for robust i-vector speaker verification , 2016, 2016 IEEE Spoken Language Technology Workshop (SLT).