Regularized Information Preserving Projections

In hyperspectral data classification, too many bands cause overfitting. Techniques for reducing bands are either informative or discriminant. Each technique has its strengths and weaknesses. Motivated by Gaussian processes latent variable models, we propose a linear projection technique that is both informative and discriminant. The technique optimizes a regularized information preserving objective, where regularization sets a preference for latent variables. Experimental results based on hyperspectral image data are provided to validate the proposed technique.

[1]  Aleksandra Pizurica,et al.  Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Chein-I Chang,et al.  Kalman filtering approach to multispectral/hyperspectral image classification , 1999 .

[3]  David A. Landgrebe,et al.  Hyperspectral Image Data Analysis as a High Dimensional Signal Processing Problem , 2002 .

[4]  Mehrtash Tafazzoli Harandi,et al.  Joint Dimensionality Reduction and Metric Learning: A Geometric Take , 2017, ICML.

[5]  Jerome H. Friedman,et al.  Flexible Metric Nearest Neighbor Classification , 1994 .

[6]  Erik Blasch,et al.  Hyperspectral imagery throughput and fusion evaluation over compression and interpolation , 2008, 2008 11th International Conference on Information Fusion.

[7]  Qian Du,et al.  Automatic target recognition for hyperspectral imagery using high-order statistics , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Haesun Park,et al.  Generalizing discriminant analysis using the generalized singular value decomposition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Chein-I Chang,et al.  Constrained band selection for hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[12]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[13]  Jing Peng,et al.  Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Bahareh Abolhasanzadeh,et al.  Gaussian process latent variable model for dimensionality reduction in intrusion detection , 2015, 2015 23rd Iranian Conference on Electrical Engineering.

[15]  Qi Tian,et al.  Similarity Gaussian Process Latent Variable Model for Multi-modal Data Analysis , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  Junbin Gao,et al.  Supervised Latent Linear Gaussian Process Latent Variable Model for Dimensionality Reduction , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  C. Bishop,et al.  Analysis of multiphase flows using dual-energy gamma densitometry and neural networks , 1993 .

[18]  Michael Elad,et al.  Optimal reduced-rank quadratic classifiers using the Fukunaga-Koontz transform with applications to automated target recognition , 2003, SPIE Defense + Commercial Sensing.

[19]  LinLin Shen,et al.  Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[21]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[22]  Tao Wang,et al.  Bio-Inspired Adaptive Hyperspectral Imaging for Real-Time Target Tracking , 2010, IEEE Sensors Journal.

[23]  Mikhail F. Kanevski,et al.  A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[24]  Uwe Weidner,et al.  Classification in High-Dimensional Feature Spaces—Assessment Using SVM, IVM and RVM With Focus on Simulated EnMAP Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[26]  Alireza Sarveniazi.  An Actual Survey of Dimensionality Reduction , 2014 .

[27]  Trevor Darrell,et al.  Discriminative Gaussian process latent variable model for classification , 2007, ICML '07.

[28]  Xuelong Li,et al.  Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Chein-I Chang,et al.  Automatic spectral target recognition in hyperspectral imagery , 2003 .

[30]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[31]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[32]  Xiuwen Liu,et al.  A hybrid PCA-LDA model for dimension reduction , 2011, The 2011 International Joint Conference on Neural Networks.

[33]  Qi Tian,et al.  Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[34]  Yaoliang Yu,et al.  Learning Latent Space Models with Angular Constraints , 2017, ICML.

[35]  Lei Huang,et al.  Age estimation based on improved discriminative Gaussian process latent variable model , 2015, Multimedia Tools and Applications.

[36]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[37]  Maja Pantic,et al.  Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition , 2015, IEEE Transactions on Image Processing.

[38]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[39]  Sayan Mukherjee,et al.  Adaptive Randomized Dimension Reduction on Massive Data , 2015, J. Mach. Learn. Res..