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Abstract:
Dictionary in Local Coordinate Coding (LCC) is important to approximate a non-linear function with linear ones. Optimizing dictionary from predefined coding schemes is a challenge task. This paper focuses on learning dictionary from two Locality Coding Adaptors (LCAs), i.e., locality Gaussian Adaptor (GA) and locality Euclidean Adaptor (EA), for large-scale and high-dimension datasets. Online dictionary learning is formulated as two cycling steps, local coding and dictionary updating. Both stages scale up gracefully to large-scale datasets with millions of data. The experiments on different applications demonstrate that our method leads to a faster dictionary learning than the classical ones or the state-of-the-art methods. (C) 2015 Elsevier B.V. All rights reserved.
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NEUROCOMPUTING
ISSN: 0925-2312
Year: 2015
Volume: 157
Page: 61-69
6 . 0 0 0
JCR@2022
ESI Discipline: COMPUTER SCIENCE;
ESI HC Threshold:168
JCR Journal Grade:1
CAS Journal Grade:3
Cited Count:
WoS CC Cited Count: 3
SCOPUS Cited Count: 3
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2