Journal Article10.1016/J.NEUCOM.2016.08.144
Image categorization using non-negative kernel sparse representation
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TL;DR: Experimental results show that by exploiting the non-linear structure in images and utilizing the ‘additive’ nature of non-negative sparse coding, promising classification performance can be obtained.
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About: This article is published in Neurocomputing. The article was published on 20 Dec 2017. The article focuses on the topics: Sparse approximation & K-SVD.
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References
Learning the parts of objects by non-negative matrix factorization
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Atomic Decomposition by Basis Pursuit
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Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
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Learning parts of objects by non-negative matrix factorization
D. D. Lee
- 01 Jan 1999
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
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