Results 1 - 10 of 202,104

Efficient sparse coding algorithms

by Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng - In NIPS , 2007
"... Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level features in the data. However, finding sparse codes remains a very difficult computational problem. In this paper, we ..."
Abstract - Cited by 440 (14 self) - Add to MetaCart
Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level features in the data. However, finding sparse codes remains a very difficult computational problem. In this paper, we

Linear spatial pyramid matching using sparse coding for image classification

by Jianchao Yang, Kai Yu, Yihong Gong, Thomas Huang - in IEEE Conference on Computer Vision and Pattern Recognition(CVPR , 2009
"... Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algo ..."
Abstract - Cited by 488 (19 self) - Add to MetaCart
the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably

Sparse coding with an overcomplete basis set: a strategy employed by V1

by Bruno A. Olshausen, David J. Fieldt - Vision Research , 1997
"... The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and ban@ass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive f ..."
Abstract - Cited by 954 (12 self) - Add to MetaCart
field properties may be accounted for in terms of a strategy for producing a sparse distribution of output activity in response to natural images. Here, in addition to describing this work in a more expansive fashion, we examine the neurobiological implications of sparse coding. Of particular interest

Non-negative sparse coding, in

by Patrik O. Hoyer - Proc. IEEE Workshop on Neural Networks for Signal Processing (NNSP’2002), 2002
"... Abstract. Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and nonnegative matrix factorization. We then gi ..."
Abstract - Cited by 167 (3 self) - Add to MetaCart
Abstract. Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and nonnegative matrix factorization. We

Online dictionary learning for sparse coding

by Julien Mairal, Francis Bach, Jean Ponce , Guillermo Sapiro , 2009
"... Sparse coding — that is, modelling data vectors as sparselinearcombinationsofbasiselements—is widelyusedinmachinelearning,neuroscience, signalprocessing,andstatistics. Thispaperfocusesonlearningthebasisset, alsocalleddictionary,toadaptittospecificdata,anapproach thathasrecentlyproventobeveryeffecti ..."
Abstract - Cited by 239 (23 self) - Add to MetaCart
Sparse coding — that is, modelling data vectors as sparselinearcombinationsofbasiselements—is widelyusedinmachinelearning,neuroscience, signalprocessing,andstatistics. Thispaperfocusesonlearningthebasisset, alsocalleddictionary

Online learning for matrix factorization and sparse coding

by Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro , 2010
"... Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set in order to ad ..."
Abstract - Cited by 317 (31 self) - Add to MetaCart
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set in order

Sparse Coding

by Themis Prodromakis, Shimeng Yu, Doo Seok Jeong, Sapan Agarwal, James B. Aimone, Agarwal S, Quach T-t, Parekh O, Hsia Ah, Debenedictis Ep, James Cd, Sapan Agarwal, Tu-thach Quach, Ojas Parekh, Er H. Hsia, Erik P. Debenedictis, Conrad D. James, Matthew J. Marinella, James B. Aimone , 2015
"... This article was submitted to ..."
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This article was submitted to

Sparse Coding of Music Signals

by Samer A. Abdallah, Mark D. Plumbley , 2001
"... We discuss the use of unsupervised learning techniques for the perception of music, focussing on the sparse coding of audio spectrograms. ..."
Abstract - Cited by 4 (3 self) - Add to MetaCart
We discuss the use of unsupervised learning techniques for the perception of music, focussing on the sparse coding of audio spectrograms.

On Shift-Invariant Sparse Coding

by Thomas Blumensath, Mike Davies - PRIETO (EDS.), INDEPENDENT COMPONENT ANALYSIS AND BLIND SIGNAL SEPARATION: PROC. FIFTH INTL. CONF., ICA 2004 , 2004
"... The goals of this paper are: 1) the introduction of a shiftinvariant sparse coding model together with learning rules for this model; 2) the comparison of this model to the traditional sparse coding model; and 3) the analysis of some limitations of the newly proposed approach. To evaluate ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
The goals of this paper are: 1) the introduction of a shiftinvariant sparse coding model together with learning rules for this model; 2) the comparison of this model to the traditional sparse coding model; and 3) the analysis of some limitations of the newly proposed approach. To evaluate

Self-Organizing Sparse Codes

by Yangqing Jia, Sergey Karayev
"... Sparse coding as applied to natural image patches learns Gabor-like components that resemble those found in the lower areas of the visual cortex. This biological motivation for sparse coding would also suggest that the learned receptive field elements be organized spatially by their response propert ..."
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Sparse coding as applied to natural image patches learns Gabor-like components that resemble those found in the lower areas of the visual cortex. This biological motivation for sparse coding would also suggest that the learned receptive field elements be organized spatially by their response
Results 1 - 10 of 202,104