Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?

[1]  Yuhai Wu,et al.  Statistical Learning Theory , 2021, Technometrics.

[2]  Jian Yang,et al.  Robust, discriminative and comprehensive dictionary learning for face recognition , 2018, Pattern Recognit..

[3]  Xuelong Li,et al.  Subspace clustering guided convex nonnegative matrix factorization , 2018, Neurocomputing.

[4]  Yizhou Yu,et al.  Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  G. Sapiro,et al.  Dissimilarity-Based Sparse Subset Selection , 2016, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Lei Zhang,et al.  A Probabilistic Collaborative Representation Based Approach for Pattern Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Mihai Datcu,et al.  Discriminative Nonnegative Matrix Factorization for dimensionality reduction , 2016, Neurocomputing.

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

[9]  Fahad Shahbaz Khan,et al.  Recognizing Actions Through Action-Specific Person Detection , 2015, IEEE Transactions on Image Processing.

[10]  Lei Zhang,et al.  Towards effective codebookless model for image classification , 2015, Pattern Recognit..

[11]  Subhransu Maji,et al.  Deep filter banks for texture recognition and segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Marcel Simon,et al.  Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Subhransu Maji,et al.  Bilinear CNN Models for Fine-Grained Visual Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Simon C. K. Shiu,et al.  Effective texture classification by texton encoding induced statistical features , 2015, Pattern Recognit..

[15]  Luc Van Gool,et al.  Learned Collaborative Representations for Image Classification , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[16]  Masayuki Mukunoki,et al.  Discriminative Collaborative Representation for Classification , 2014, ACCV.

[17]  Naila Murray,et al.  Revisiting the Fisher vector for fine-grained classification , 2014, Pattern Recognit. Lett..

[18]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[19]  Fatih Murat Porikli,et al.  Classification and Boosting with Multiple Collaborative Representations , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Luc Van Gool,et al.  Adaptive and Weighted Collaborative Representations for image classification , 2014, Pattern Recognit. Lett..

[21]  Pietro Perona,et al.  Bird Species Categorization Using Pose Normalized Deep Convolutional Nets , 2014, ArXiv.

[22]  Naila Murray,et al.  Generalized Max Pooling , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[24]  David Zhang,et al.  Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification , 2014, International Journal of Computer Vision.

[25]  Jiwen Lu,et al.  PCANet: A Simple Deep Learning Baseline for Image Classification? , 2014, IEEE Transactions on Image Processing.

[26]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[27]  Masayuki Mukunoki,et al.  Collaborative Representation for Classification, Sparse or Non-sparse? , 2014, ArXiv.

[28]  Simon C. K. Shiu,et al.  Fast and robust face recognition via coding residual map learning based adaptive masking , 2014, Pattern Recognit..

[29]  Jonathan Krause,et al.  3D Object Representations for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[30]  Andrew Zisserman,et al.  Symbiotic Segmentation and Part Localization for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision.

[31]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[32]  Larry S. Davis,et al.  Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Yan Liu,et al.  Joint discriminative dimensionality reduction and dictionary learning for face recognition , 2013, Pattern Recognit..

[34]  Cordelia Schmid,et al.  Expanded Parts Model for Human Attribute and Action Recognition in Still Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Peter N. Belhumeur,et al.  POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Shenghuo Zhu,et al.  Efficient Object Detection and Segmentation for Fine-Grained Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Dieter Fox,et al.  Multipath Sparse Coding Using Hierarchical Matching Pursuit , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Subhransu Maji,et al.  Fine-Grained Visual Classification of Aircraft , 2013, ArXiv.

[39]  Yujin Zhang,et al.  Nonnegative Matrix Factorization: A Comprehensive Review , 2013, IEEE Transactions on Knowledge and Data Engineering.

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

[41]  Matthias Hein,et al.  Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization , 2012, 1205.0953.

[42]  Stéphane Mallat,et al.  Invariant Scattering Convolution Networks , 2012, IEEE transactions on pattern analysis and machine intelligence.

[43]  Jian Yang,et al.  Beyond sparsity: The role of L1-optimizer in pattern classification , 2012, Pattern Recognit..

[44]  Matthias Hein,et al.  Sparse recovery by thresholded non-negative least squares , 2011, NIPS.

[45]  Leonidas J. Guibas,et al.  Human action recognition by learning bases of action attributes and parts , 2011, 2011 International Conference on Computer Vision.

[46]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

[47]  Andrew Zisserman,et al.  BiCoS: A Bi-level co-segmentation method for image classification , 2011, 2011 International Conference on Computer Vision.

[48]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[49]  Thomas S. Huang,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Pietro Perona,et al.  The Caltech-UCSD Birds-200-2011 Dataset , 2011 .

[51]  Vincent Lepetit,et al.  Are sparse representations really relevant for image classification? , 2011, CVPR 2011.

[52]  Qi Tian,et al.  Image classification by non-negative sparse coding, low-rank and sparse decomposition , 2011, CVPR 2011.

[53]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[54]  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.

[55]  Liang-Tien Chia,et al.  Local features are not lonely – Laplacian sparse coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[56]  Chengjun Liu,et al.  Color space normalization: Enhancing the discriminating power of color spaces for face recognition , 2010, Pattern Recognit..

[57]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[58]  Rainer Stiefelhagen,et al.  Why Is Facial Occlusion a Challenging Problem? , 2009, ICB.

[59]  A. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[60]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[61]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[62]  Andrzej Cichocki,et al.  Nonnegative matrix factorization with quadratic programming , 2008, Neurocomputing.

[63]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[64]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[65]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[67]  Yihong Gong,et al.  Document clustering by concept factorization , 2004, SIGIR '04.

[68]  Stan Z. Li,et al.  Learning spatially localized, parts-based representation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[69]  P. Belhumeur,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[70]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[71]  R. Bro,et al.  A fast non‐negativity‐constrained least squares algorithm , 1997 .

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

[73]  L. Breiman.  Better subset regression using the nonnegative garrote , 1995 .

[74]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[75]  Dimitri P. Bertsekas,et al.  On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..

[76]  Stanley Win Kler,et al.  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2015 .

[77]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints Abstract by Matthijs Dorst Based on the paper by , 2011 .

[78]  Chris H. Q. Ding,et al.  Convex and Semi-Nonnegative Matrix Factorizations , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[79]  P. Belhumeur,et al.  Eigenfaces vs . Fisherfaces : Recognition Using Class Speci c Linear Projection , 2001 .

[80]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

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

[82]  A. Martínez,et al.  The AR face databasae , 1998 .

[83]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[84]  INVERSE PROBLEMS NEWSLETTER , 1997 .

[85]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[86]  R. Courant.  Variational methods for the solution of problems of equilibrium and vibrations , 1943 .