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Sparse Recovery Using Sparse Matrices [PDF]
Proceedings of the IEEE, 2010 In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals.
Piotr Indyk, Anna C Gilbert
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SC2Net: Sparse LSTMs for Sparse Coding
Proceedings of the AAAI Conference on Artificial Intelligence, 2018The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due
Joey Tianyi Zhou +9 more
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Sparse Topical Coding with Sparse Groups
2016Learning a latent semantic representing from a large number of short text corpora makes a profound practical significance in research and engineering. However, it is difficult to use standard topic models in microblogging environments since microblogs have short length, large amount, snarled noise and irregular modality characters, which prevent topic ...
Min Peng 0002 +6 more
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Sparse Coding in Sparse Winner Networks
2007This paper investigates a mechanism for reliable generation of sparse code in a sparsely connected, hierarchical, learning memory. Activity reduction is accomplished with local competitions that suppress activities of unselected neurons so that costly global competition is avoided.
Janusz A. Starzyk +2 more
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2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost.
Zhen James Xiang, Peter J. Ramadge
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We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost.
Zhen James Xiang, Peter J. Ramadge
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International Journal of Foundations of Computer Science, 1999
PEI formalism has been designed to reason and develop parallel programs in the context of data parallelism. In this paper, we focus on the use of PEI to transform a program involving dense matrices into a new program involving sparse matrices, using the example of the matrix-vector product.
Frédérique Voisin, Guy-René Perrin
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PEI formalism has been designed to reason and develop parallel programs in the context of data parallelism. In this paper, we focus on the use of PEI to transform a program involving dense matrices into a new program involving sparse matrices, using the example of the matrix-vector product.
Frédérique Voisin, Guy-René Perrin
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Sparse Autoencoder for Sparse Code Multiple Access
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2021In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum.
Medini Singh +2 more
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Non-linear sparse and group sparse classifier
2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2013Recently there has been an interest in a new classification model, where it is assumed that the training samples for a particular class form a linear basis for any new test sample belonging to that class. This assumption led to two successful classification methods called the Sparse Classifier (SC) and the Group Sparse Classifier (GSC).
Angshul Majumdar, Rabab K. Ward
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