Algorithm Optimization Using Features In SVD & Classification In Eigenspace

Singular Value Decomposition (SVD) is ubiquitous in a range of applications including computer science, economics, engineering, geology, oceanography, psychology, social networking etc. It is an unsupervised modeling technique that creates latent vectors for a subspace that reduces the dimensionality of observed data from n to k (k<<n) dimensions. Latent variables are uncorrelated variation of attribute values that are correlated in the original space. Moreover, SVD can be used to detect/remove noise/outliers, cluster similar entities and make predictions. On the other hand, classification tree is a supervised technique that accomplishes the similar tasks. It models decision trees from training data in order to make intelligent predictions. There is a close connection between SVD and decision trees, but differ in purpose, algorithm design and error analysis techniques. We present a hybrid algorithm bridges the gap between these standalone algorithms and adaptively supersedes their outcomes. For experimental analysis, we use realworld benchmark data, wines, publicly available from UCI machine learning repository. The algorithm is implemented in Matlab, supported by decision trees in Weka software, on MacOS Seirra Version 10.12.3 8GB 160MHZ.

[1]  M. L. M. S. Binoti.  Emprego de Redes Neurais Artificiais em Mensuração e Manejo Florestal , 2012 .

[2]  Yue Yang,et al.  Complex network-based time series analysis , 2008 .

[3]  Giles M. Foody,et al.  Mapping the biomass of Bornean tropical rain forest from remotely sensed data , 2001 .

[4]  Jun Yu,et al.  Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Akira Imada,et al.  A Literature Review: Forest Management with Neural Network and Artificial Intelligence , 2014 .

[6]  H. Pretzsch.  Forest Dynamics, Growth, and Yield , 2010 .

[7]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[8]  Edna Lúcia Flôres,et al.  Recursive diameter prediction for calculating merchantable volume of eucalyptus clones using Multilayer Perceptron , 2012, Neural Computing and Applications.

[9]  Gang Feng,et al.  Time series analysis of the developed financial markets' integration using visibility graphs , 2014 .

[10]  Stefano Battiston,et al.  The Network of Global Corporate Control , 2011, PloS one.

[11]  Jaime Wojciechowski,et al.  Comparison of data mining and allometric model in estimation of tree biomass , 2015, BMC Bioinformatics.

[12]  F. Rovero,et al.  Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics , 2013 .

[13]  Carlos Soares,et al.  Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results , 2003, Machine Learning.

[14]  C. Sanquetta,et al.  Biomass expansion factor and root-to-shoot ratio for Pinus in Brazil , 2011, Carbon balance and management.

[15]  Luis F. Ortega.  A Neuro-wavelet Method for the Forecasting of Financial Time Series , 2012 .

[16]  Aleodor Daniel Ioan,et al.  Designing an optimal single chip FPGA video interface for embedded systems , 2010, 2010 3rd International Symposium on Electrical and Electronics Engineering (ISEEE).

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[19]  Flávio Felipe Kirchner,et al.  Estimativa de biomassa e carbono em floresta com araucaria utilizando imagens do satélite Ikonos II. , 2009 .

[20]  Yassine Ruichek,et al.  Occupancy Grid Mapping in Urban Environments from a Moving On-Board Stereo-Vision System , 2014, Sensors.

[21]  Helio Garcia Leite,et al.  Crescimento e produção de plantios comerciais de eucalipto estimados por duas categorias de modelos , 2013 .

[22]  Wang Rui,et al.  A laser scanning data acquisition and display system based on ROS , 2014, Proceedings of the 33rd Chinese Control Conference.

[23]  Yan Zhang,et al.  Designing of VGA Character String Display Module Based on FPGA , 2009, 2009 International Symposium on Intelligent Ubiquitous Computing and Education.

[24]  Yue Yang,et al.  Visibility graph approach to exchange rate series , 2009 .

[25]  Maria J. Diamantopoulou,et al.  Predicting fir trees stem diameters using Artificial Neural Network models , 2005 .

[26]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[27]  R. Ansley,et al.  Evaluating Biomass of Juniper Trees (Juniperus pinchotii) from Imagery-Derived Canopy Area Using the Support Vector Machine Classifier , 2013 .

[28]  N. K. Geetha,et al.  Graph Theory Matrix Approach - A Review , 2016 .

[29]  K. Macdicken,et al.  Global Forest Resources Assessment 2015: how are the world's forests changing? , 2015 .

[30]  Philip C. Treleaven,et al.  Algorithmic trading review , 2013, CACM.

[31]  Carlos Roberto Sanquetta,et al.  On the use of data mining for estimating carbon storage in the trees , 2013, Carbon Balance and Management.

[32]  A. P. Corte,et al.  Estimativas de biomassa para plantas de bambu do gênero Guadua , 2014 .

[33]  M. Newman.  Mathematics of networks , 2018, Oxford Scholarship Online.

[34]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[35]  Daniel Henrique Breda Binoti,et al.  Estimation of inside-bark diameter and heartwood diameter for Tectona grandis Linn. trees using artificial neural networks , 2011, European Journal of Forest Research.

[36]  Jason W. Osbourne,et al.  Four Assumptions of Multiple Regression That Researchers Should Always Test. , 2002 .

[37]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[38]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[39]  Duncan G. Elliott,et al.  Computational RAM: Implementing Processors in Memory , 1999, IEEE Des. Test Comput..

[40]  Helio Garcia Leite,et al.  Estimativa do estoque de biomassa em um fragmento florestal usando imagens orbitais , 2014 .

[41]  Chaman L. Sabharwal,et al.  An SVD-Entropy and bilinearity based product ranking algorithm using heterogeneous data , 2017, J. Vis. Lang. Comput..

[42]  F. Schumacher.  Logarithmic expression of timber-tree volume , 1933 .

[43]  A. Snarskii,et al.  From the time series to the complex networks: The parametric natural visibility graph , 2012, 1208.6365.

[44]  M. Elliott,et al.  Financial Networks and Contagion , 2014 .

[45]  Britta Ruhnau,et al.  Eigenvector-centrality - a node-centrality? , 2000, Soc. Networks.

[46]  Xuan-Tu Tran,et al.  An efficient architecture design for VGA monitor controller , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).

[47]  Shijie Yan,et al.  Time Series Analysis Based on Visibility Graph Theory , 2015, 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[48]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Uma Introdução às Support Vector Machines , 2007, RITA.