Fundamentals of Robust Representations

This chapter presents the fundamentals of robust representations. In particular, we provide a brief overview of existing representation learning and robust representation methods. The advantages and disadvantages of these existing methods are also discussed.

[1]  Thomas Seidl,et al.  SMVC: semi-supervised multi-view clustering in subspace projections , 2014, KDD.

[2]  Shuicheng Yan,et al.  Robust and Efficient Subspace Segmentation via Least Squares Regression , 2012, ECCV.

[3]  Rama Chellappa,et al.  Cross-View Action Recognition via a Transferable Dictionary Pair , 2012, BMVC.

[4]  Yong Yu,et al.  Robust Subspace Segmentation by Low-Rank Representation , 2010, ICML.

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

[6]  Zhang Yi,et al.  Scalable Sparse Subspace Clustering , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Rama Chellappa,et al.  Information-Theoretic Dictionary Learning for Image Classification , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Francis R. Bach,et al.  Consistency of trace norm minimization , 2007, J. Mach. Learn. Res..

[9]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Jeff A. Bilmes,et al.  On Deep Multi-View Representation Learning , 2015, ICML.

[12]  Yi Ma,et al.  TILT: Transform Invariant Low-Rank Textures , 2010, ACCV 2010.

[13]  Larry S. Davis,et al.  Discriminative Dictionary Learning with Pairwise Constraints , 2012, ACCV.

[14]  Jianmin Zhao,et al.  Low-Rank Matrix Recovery with Discriminant Regularization , 2013, PAKDD.

[15]  Ameet Talwalkar,et al.  Distributed Low-Rank Subspace Segmentation , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Robert M. Haralick,et al.  Second-Order Bilinear Discriminant Analysis , 2010, J. Mach. Learn. Res..

[17]  Yong Yu,et al.  Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

[19]  Ming Shao,et al.  Multi-View Low-Rank Analysis for Outlier Detection , 2015, SDM.

[20]  Zhihua Zhang,et al.  Exact Subspace Clustering in Linear Time , 2014, AAAI.

[21]  R. Fisher THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS , 1938 .

[22]  Jian Yang,et al.  Supervised and Unsupervised Parallel Subspace Learning for Large-Scale Image Recognition , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Yu-Chiang Frank Wang,et al.  Low-rank matrix recovery with structural incoherence for robust face recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Paulo R. S. Mendonça,et al.  Model-Based Hand Tracking Using an Unscented Kalman Filter , 2001, BMVC.

[25]  Larry S. Davis,et al.  Learning Structured Low-Rank Representations for Image Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Yun Fu,et al.  Robust Subspace Discovery through Supervised Low-Rank Constraints , 2014, SDM.

[27]  Chunheng Wang,et al.  Sparse representation for face recognition based on discriminative low-rank dictionary learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Shiguang Shan,et al.  Multi-View Discriminant Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Chao Lan,et al.  Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning , 2015, KDD.

[30]  Qionghai Dai,et al.  Low-Rank Structure Learning via Nonconvex Heuristic Recovery , 2010, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Shuicheng Yan,et al.  Active Subspace: Toward Scalable Low-Rank Learning , 2012, Neural Computation.

[32]  Lucas C. Parra,et al.  Bilinear Discriminant Component Analysis , 2007, J. Mach. Learn. Res..

[33]  Yun Fu,et al.  Learning low-rank and discriminative dictionary for image classification , 2014, Image Vis. Comput..

[34]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.

[35]  Ming Shao,et al.  Cross-View Projective Dictionary Learning for Person Re-Identification , 2015, IJCAI.

[36]  Xiaowei Zhou,et al.  Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Yun Fu,et al.  Learning Robust and Discriminative Subspace With Low-Rank Constraints , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Lei Zhang,et al.  Projective dictionary pair learning for pattern classification , 2014, NIPS.

[39]  Yun Fu,et al.  Learning Balanced and Unbalanced Graphs via Low-Rank Coding , 2015, IEEE Transactions on Knowledge and Data Engineering.

[40]  Hai Jin,et al.  MsLRR: A Unified Multiscale Low-Rank Representation for Image Segmentation , 2014, IEEE Transactions on Image Processing.

[41]  Kun Zhou,et al.  Locality Sensitive Discriminant Analysis , 2007, IJCAI.

[42]  Feiping Nie,et al.  Large-Scale Multi-View Spectral Clustering via Bipartite Graph , 2015, AAAI.

[43]  Jiebo Luo,et al.  Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization , 2013, IEEE Transactions on Image Processing.

[44]  I. Jolliffe Principal Component Analysis and Factor Analysis , 1986 .

[45]  Shuicheng Yan,et al.  Latent Low-Rank Representation for subspace segmentation and feature extraction , 2011, 2011 International Conference on Computer Vision.

[46]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[47]  Aleix M. Martínez,et al.  Subclass discriminant analysis , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  Chongyu Chen,et al.  Surveillance video coding via low-rank and sparse decomposition , 2012, ACM Multimedia.

[49]  Xiao-Tong Yuan,et al.  Sparse Additive Subspace Clustering , 2014, ECCV.

[50]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Yuhong Guo,et al.  Convex Subspace Representation Learning from Multi-View Data , 2013, AAAI.

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

[53]  Rudolf Mester,et al.  Discriminative Subspace Clustering , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[54]  H. Hotelling Relations Between Two Sets of Variates , 1936 .

[55]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[56]  Jian Yang,et al.  Low-rank representation based discriminative projection for robust feature extraction , 2013, Neurocomputing.

[57]  Jianjiang Feng,et al.  Smooth Representation Clustering , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.