Efficient Kernel Sparse Coding Via First-Order Smooth Optimization

We consider the problem of dictionary learning and sparse coding, where the task is to find a concise set of basis vectors that accurately represent the observation data with only small numbers of active bases. Typically formulated as an L1-regularized least-squares problem, the problem incurs compu...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 25; číslo 8; s. 1447 - 1459
Hlavní autor:	Kim, Minyoung
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.08.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Approximation algorithms Approximation methods Artificial intelligence Coding Coding, codes Computer science; control theory; systems Data processing. List processing. Character string processing Dictionaries Dictionary learning Encoding Exact sciences and technology Exact solutions first-order smooth optimization Information, signal and communications theory Kernel kernel representer theorem Kernels Learning Memory organisation. Data processing Neural networks Optimization Optimization techniques Pattern recognition. Digital image processing. Computational geometry Signal and communications theory Software sparse coding Telecommunications and information theory Theorems Theoretical computing Vectors kernel representer theorem sparse coding first-order smooth optimization Dictionary learning Dictionaries Sparse coding Computational complexity Optimization Kernel method Kernel function Linear coding Efficiency Least squares method Optimal code Observation data Sparse representation Representation theory Large scale Learning algorithm Artificial intelligence
ISSN:	2162-237X, 2162-2388, 2162-2388
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