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Sparse Representation in Kernel Machines
IEEE Transactions on Neural Networks and Learning Systems, 2015We 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, 1998We 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), 2011Gait 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 +3 more
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An efficient representation for sparse sets
ACM Letters on Programming Languages and Systems, 1993Sets 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, 2011In 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 +2 more
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Quantized dictionary for sparse representation
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), 2015Dictionary 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 +4 more
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Sparse Representation for Speaker Identification
2010 20th International Conference on Pattern Recognition, 2010We 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 +2 more
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Sparse Representation Shape Models
Journal of Mathematical Imaging and Vision, 2012It 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 +3 more
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Sparse reconstruction of ISOMAP representations
Journal of Intelligent & Fuzzy Systems, 2019Isometric feature mapping (ISOMAP) is one of the classical methods of nonlinear dimensionality reduction (NLDR) and seeks for low dimensional (LD) structure of high dimensional (HD) data. However, the inverse problem of ISOMAP has never been investigated, which recovers the HD sample from the related LD sample, and its application prospect in data ...
Honggui Li, Maria Trocan
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Discriminative sparse representations with applications
2013 American Control Conference, 2013Significant advances in compressive sensing and sparse signal encoding have provided a rich set of mathematical tools for signal analysis and representation. In addition to novel formulations for enabling sparse solutions to underdetermined systems, exciting progress has taken place in efficiently solving these problems from an optimization theoretic ...
Vishal Monga, Trac D. Tran
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