Results 271 to 280 of about 114,572 (313)

Grassmannian sparse representations

Journal of Electronic Imaging, 2015
We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory ...
Sherif Azary, Andreas E. Savakis
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On sparse signal representations

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002
An elementary proof of a basic uncertainty principle concerning pairs of representations of /spl Rscr//sup N/ vectors in different orthonormal bases is provided. The result, slightly stronger than stated before, has a direct impact on the uniqueness property of the sparse representation of such vectors using pairs of orthonormal bases as overcomplete ...
Michael Elad, Alfred M. Bruckstein
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Sparse Representation in Kernel Machines

IEEE Transactions on Neural Networks and Learning Systems, 2015
We study the properties of least square kernel regression with l1 coefficient regularization. The kernels can be flexibly chosen to be either positive definite or indefinite. Asymptotic learning rates are deduced under smoothness condition on the kernel. Sparse representation of the solution is characterized theoretically.
Hongwei Sun, Qiang Wu 0003
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A Sparse Representation for Function Approximation

Neural Computation, 1998
We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to principal component analysis, regularization, sparsity principles, and support vector machines.
Tomaso A. Poggio, Federico Girosi
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Gait identification by sparse representation

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
Gait recognition under variations of clothing and carrying condition is still a challenging task. In this paper, we present a gait identification method via sparse representation. We formulate the recognition problem as finding the coefficients of linear combination of the training samples plus an error term and discuss sparse signal representation ...
Minyan Gong, Yi Xu 0001, Xiaokang Yang 0001, Wenjun Zhang 0001   +3 more
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An efficient representation for sparse sets

ACM Letters on Programming Languages and Systems, 1993
Sets are a fundamental abstraction widely used in programming. Many representations are possible, each offering different advantages. We describe a representation that supports constant-time implementations of clear-set, add-member, and delete-member . Additionally, it supports an efficient
Preston Briggs, Linda Torczon
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Learning the sparse representation for classification

2011 IEEE International Conference on Multimedia and Expo, 2011
In this work, we propose a novel supervised matrix factorization method used directly as a multi-class classifier. The coefficient matrix of the factorization is enforced to be sparse by l 1 -norm regularization. The basis matrix is composed of atom dictionaries from different classes, which are trained in a jointly supervised manner by penalizing ...
Jianchao Yang, Jiangping Wang, Thomas S. Huang   +2 more
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Quantized dictionary for sparse representation

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), 2015
Dictionary learning for sparse representation has drawn considerable attention in recent years. In particular, the K-SVD algorithm is an efficient approach, and various modifications of the K-SVD have been developed for applications such as face recognition. However, the efficient storage of the dictionary has not been studied.
Lei Liu, Jie Liang 0001, Yao Zhao 0001, Chunyu Lin, Huihui Bai 0001   +4 more
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Sparse Representation for Speaker Identification

2010 20th International Conference on Pattern Recognition, 2010
We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an over complete dictionary using the GMM mean super vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database.
Imran Naseem, Roberto Togneri, Mohammed Bennamoun   +2 more
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Sparse Representation Shape Models

Journal of Mathematical Imaging and Vision, 2012
It is well-known that, during shape extraction, enrolling an appropriate shape constraint model could effectively improve locating accuracy. In this paper, a novel deformable shape model, Sparse Representation Shape Models (SRSM), is introduced. Rather than following commonly utilized statistical shape constraints, our model constrains shape appearance
Yuelong Li, Jufu Feng, Li Meng, Jigang Wu   +3 more
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