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68,881
Efficient sparse coding algorithms
- 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 445 (14 self)
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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
- 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 497 (21 self)
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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
- 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 958 (9 self)
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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
- PROC. IEEE WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING (NNSP’2002), 2002
, 2002
"... 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 give a sim ..."
Abstract
-
Cited by 166 (3 self)
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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 give a
Online dictionary learning for sparse coding
, 2009
"... Sparse coding — that is, modelling data vectors as sparselinearcombinationsofbasiselements—is widelyusedinmachinelearning,neuroscience, signalprocessing,andstatistics. Thispaperfocusesonlearningthebasisset, alsocalleddictionary,toadaptittospecificdata,anapproach thathasrecentlyproventobeveryeffecti ..."
Abstract
-
Cited by 246 (22 self)
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Sparse coding — that is, modelling data vectors as sparselinearcombinationsofbasiselements—is widelyusedinmachinelearning,neuroscience, signalprocessing,andstatistics. Thispaperfocusesonlearningthebasisset, alsocalleddictionary
Online learning for matrix factorization and sparse coding
, 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 330 (31 self)
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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 of Music Signals
, 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)
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We discuss the use of unsupervised learning techniques for the perception of music, focussing on the sparse coding of audio spectrograms.
Sparse Coding for Object Recognition
, 2013
"... In recent years, the application of sparse coding techniques has led to frameworks that match or set the state-of-the-art in object recognition tasks. De-spite such success, applying sparse coding to vi-sion tasks presents unique challenges and many pa-pers addressing these concerns appear in top co ..."
Abstract
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In recent years, the application of sparse coding techniques has led to frameworks that match or set the state-of-the-art in object recognition tasks. De-spite such success, applying sparse coding to vi-sion tasks presents unique challenges and many pa-pers addressing these concerns appear in top
Product Sparse Coding
"... Sparse coding is a widely involved technique in com-puter vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook is ..."
Abstract
-
Cited by 2 (0 self)
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Sparse coding is a widely involved technique in com-puter vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook
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68,881
