Open AccessProceedings Article
Supervised Dictionary Learning
Julien Mairal,Jean Ponce,Guillermo Sapiro,Andrew Zisserman,Francis Bach +4 more
- 08 Dec 2008
- Vol. 21, pp 1033-1040
TL;DR: A novel sparse representation for signals belonging to different classes in terms of a shared dictionary and discriminative class models is proposed, with results on standard handwritten digit and texture classification tasks.
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Abstract: It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction, with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and discriminative class models. The linear version of the proposed model admits a simple probabilistic interpretation, while its most general variant admits an interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experimental results on standard handwritten digit and texture classification tasks.
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Citations
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Deep Sparse Rectifier Neural Networks
Xavier Glorot,Antoine Bordes,Yoshua Bengio +2 more
- 14 Jun 2011
TL;DR: This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-dierentiabil ity.
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Learning Deep Architectures for AI
Yoshua Bengio
- 01 Jan 2009
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
Linear spatial pyramid matching using sparse coding for image classification
Jianchao Yang,Kai Yu,Yihong Gong,Thomas S. Huang +3 more
- 20 Jun 2009
TL;DR: An extension of the SPM method is developed, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and a linear SPM kernel based on SIFT sparse codes is proposed, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.
Online dictionary learning for sparse coding
Julien Mairal,Francis Bach,Jean Ponce,Guillermo Sapiro +3 more
- 14 Jun 2009
TL;DR: A new online optimization algorithm for dictionary learning is proposed, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples, and leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.
Online Learning for Matrix Factorization and Sparse Coding
TL;DR: In this paper, a new online optimization algorithm based on stochastic approximations is proposed to solve the large-scale matrix factorization problem, which scales up gracefully to large data sets with millions of training samples.
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Robust Face Recognition via Sparse Representation
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Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Michael Elad,Michal Aharon +1 more
TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.
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