Learning in the compressed data domain: Application to milk quality prediction

Abstract Smart dairy farming has become one of the most exciting and challenging area in cloud-based data analytics. Transfer of raw data from all farms to a central cloud is currently not feasible as applications are generating more data while internet connectivity is lacking in rural farms. As a solution, Fog computing has become a key factor to process data near the farm and derive farm insights by exchanging data between on-farm applications and transferring some data to the cloud. In this context, learning in the compressed data domain, where de-compression is not necessary, is highly desirable as it minimizes the energy used for communication/computation, reduces required memory/storage, and improves application latency. Mid-infrared spectroscopy (MIRS) is used globally to predict several milk quality parameters as well as deriving many animal-level phenotypes. Therefore, compressed learning on MIRS data is beneficial both in terms of data processing in the Fog, as well as storing large data sets in the cloud. In this paper, we used principal component analysis and wavelet transform as two techniques for compressed learning to convert MIRS data into a compressed data domain. The study derives near lossless compression parameters for both techniques to transform MIRS data without impacting the prediction accuracy for a selection of milk quality traits.

[1]  Peter J. Haas,et al.  Compressed Linear Algebra for Large-Scale Machine Learning , 2016, Proc. VLDB Endow..

[2]  Kin-Man Lam,et al.  Facial-feature detection and localization based on a hierarchical scheme , 2014, Inf. Sci..

[3]  Sungyoung Lee,et al.  Compressive sensing: From theory to applications, a survey , 2013, Journal of Communications and Networks.

[4]  W Steeneveld,et al.  Characterization of Dutch dairy farms using sensor systems for cow management. , 2015, Journal of dairy science.

[5]  Junyu Dong,et al.  Image retrieval using wavelet-based salient regions , 2011 .

[6]  H. Vincent Poor,et al.  Attribute-Distributed Learning: Models, Limits, and Algorithms , 2011, IEEE Transactions on Signal Processing.

[7]  Chamil Kulatunga,et al.  Cooperative in-network computation in energy harvesting device clouds , 2017, Sustain. Comput. Informatics Syst..

[8]  LinLin Shen,et al.  Visual-Patch-Attention-Aware Saliency Detection , 2015, IEEE Transactions on Cybernetics.

[9]  Kin-Man Lam,et al.  Illumination-insensitive texture discrimination based on illumination compensation and enhancement , 2014, Inf. Sci..

[10]  Chamil Kulatunga,et al.  Opportunistic Wireless Networking for Smart Dairy Farming , 2017, IT Professional.

[11]  Jacqueline J Meulman,et al.  Nonlinear principal components analysis: introduction and application. , 2007, Psychological methods.

[12]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[13]  Yonina C. Eldar,et al.  Structured Compressed Sensing: From Theory to Applications , 2011, IEEE Transactions on Signal Processing.

[14]  John C. Mitchell,et al.  Compressive Feature Learning , 2013, NIPS.

[15]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[16]  M.A.P. Garcia,et al.  On-line Estimation of Fresh Milk Composition by means of VIS-NIR Spectrometry and Partial Least Squares Method (PLS) , 2008, 2008 IEEE Instrumentation and Measurement Technology Conference.

[17]  M. De Marchi,et al.  Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. , 2014, Journal of dairy science.

[18]  Lokukaluge P. Perera,et al.  Machine intelligence for energy efficient ships: A big data solution , 2016 .

[19]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Kuldip K. Paliwal,et al.  Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition , 2003, Pattern Recognit..

[21]  Hidetomo Ichihashi,et al.  Information compression effect based on PCA for reinforcement learning agents' communication , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[22]  Bin Song,et al.  Texture classification with cross-covariance matrices in compressive measurement domain , 2016, Signal Image Video Process..

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[25]  Johan Trygg,et al.  2D wavelet analysis and compression of on‐line industrial process data , 2001 .

[26]  Bjørn-Helge Mevik,et al.  Introduction to the pls Package , 2015 .

[27]  Christophe Lemetre,et al.  An introduction to artificial neural networks in bioinformatics - application to complex microarray and mass spectrometry datasets in cancer studies , 2008, Briefings Bioinform..

[28]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[29]  André Kaup,et al.  Incorporating Prior Information in Compressive Online Robust Principal Component Analysis , 2017, ArXiv.

[30]  Naveen Verma,et al.  Signal Processing With Direct Computations on Compressively Sensed Data , 2015, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[31]  D. Berry,et al.  The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows. , 2016, Journal of dairy science.

[32]  Antonio J. Plaza,et al.  Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Kin-Man Lam,et al.  A novel face-hallucination scheme based on singular value decomposition , 2013, Pattern Recognit..

[34]  Kin-Man Lam,et al.  Face-image retrieval based on singular values and potential-field representation , 2014, Signal Process..

[35]  S. Wolfert,et al.  Big Data in Smart Farming – A review , 2017 .

[36]  Daniel F. Valencia,et al.  Comparison analysis between rigrsure, sqtwolog, heursure and minimaxi techniques using hard and soft thresholding methods , 2016, 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA).

[37]  Xing Chen,et al.  Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images , 2016, J. Sensors.

[38]  Baojun Zhao,et al.  Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Yong He,et al.  Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms , 2013 .

[40]  S. Wold,et al.  PLS regression on wavelet compressed NIR spectra , 1998 .

[41]  Naveen Verma,et al.  Compressed Signal Processing on Nyquist-Sampled Signals , 2016, IEEE Transactions on Computers.

[42]  Thomas Strohmer,et al.  Measure What Should be Measured: Progress and Challenges in Compressive Sensing , 2012, ArXiv.

[43]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[44]  Gözde Gürdeniz,et al.  Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data , 2009 .

[45]  P. Garthwaite.  An Interpretation of Partial Least Squares , 1994 .

[46]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[47]  A. Laub,et al.  The singular value decomposition: Its computation and some applications , 1980 .

[48]  Donagh Berry,et al.  Evaluation of Non-linearity in MIR Spectroscopic Data for Compressed Learning , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[49]  R. Calderbank,et al.  Compressed Learning : Universal Sparse Dimensionality Reduction and Learning in the Measurement Domain , 2009 .