Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Dictionary learning by Normalized Bilateral Projection
Taro Tezuka
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2016 Volume 24 Issue 3 Pages 565-572

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Abstract

Dictionary learning is an unsupervised learning task that finds a set of template vectors that expresses input signals by sparse linear combinations. There are currently several methods for dictionary learning, for example K-SVD and MOD. In this paper, a new dictionary learning method, namely K-normalized bilateral projections (K-NBP), is proposed, which uses faster low rank approximation. Experiments showed that the method was fast and when the number of iterations was limited, it outperforms K-SVD. This indicated that the method was particularly suited to large data sets with high dimension, where each iteration takes a long time. K-NBP was applied to an image reconstruction task where images corrupted by noise were recovered using a dictionary learned from other images.

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© 2016 by the Information Processing Society of Japan
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