Vector Quantization
Vector Quantization is a unsupervised learning method that partitions a set of continuous vectors into discrete prototypes by mapping each input vector to the nearest representative in a finite set of codewords, thereby reducing data dimensionality and redundancy through clustering-based approximation.
2.9K
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5.8K
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1.6K
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Publications per year
2017–2026
358
2.9K
Teuvo Kohonen · Proceedings of the IEEE · 1990 · 8.2K citations
An Algorithm for Vector Quantizer Design
Y. Linde, A. Buzo, Robert M. Gray · IEEE Transactions on Communications · 1980 · 7.2K citations
Video Google: a text retrieval approach to object matching in videos
Visual categorization with bags of keypoints
G. Csurka · 2004 · 4.2K citations
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University of California, Santa Barbara
Santa Barbara, United States
37 h-index
175 publications
9.1K citations
AT&T (United States)
Bedminster, United States
36 h-index
131 publications
12.3K citations
Stanford University
Stanford, United States
36 h-index
162 publications
14.6K citations
University of California San Diego
San Diego, United States
23 h-index
62 publications
6.7K citations
University of Illinois Urbana-Champaign
Urbana, United States
21 h-index
45 publications
12.9K citations
National Chung Cheng University
Minxiong, Taiwan
19 h-index
50 publications
2K citations

Chinese Academy of Sciences
Beijing, China
19 h-index
37 publications
1.3K citations
University of Maryland, College Park
College Park, United States
19 h-index
50 publications
2.5K citations
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96 publications · 8.3K citations
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102 publications · 6.1K citations
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63 publications · 11.1K citations
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54 publications · 6.2K citations
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48 publications · 3.2K citations
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