K-means Quantization
K-Means Quantization is a vector quantization method that partitions continuous or high-precision data points into a finite set of representative clusters by minimizing the within-cluster variance through iterative application of the k-means clustering algorithm.
83
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6.8K
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213
Authors
102
Institutions
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2017–2026
17
Variants
K-means Vector Quantization
Parents
83
Compressing Deep Convolutional Networks using Vector Quantization
Yunchao Gong, Liu Liu, Ming–Hsuan Yang et al. · arXiv (Cornell University) · 2014 · 1K citations · Full text
SOM-based data visualization methods
Juha Vesanto · Intelligent Data Analysis · 1999 · 744 citations
Convergence Properties of the K-Means Algorithms
Léon Bottou, Yoshua Bengio · 1994 · 389 citations
1–5 of 83
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Louisiana State University in Shreveport, United States
10 publications · 429 citations
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1–5 of 213
102

Louisiana State University in Shreveport
Shreveport, United States
7 h-index
8 publications
377 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
4 publications
83 citations
University of Central Arkansas
Conway, United States
3 h-index
7 publications
220 citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
6 publications
153 citations
Conway School of Landscape Design
Conway, United States
3 h-index
7 publications
220 citations
University of Antwerp
Antwerp, Belgium
2 h-index
2 publications
345 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
2 h-index
5 publications
151 citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
6 publications
1.3K citations
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3 publications · 85 citations
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2 publications · 28 citations
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Repository · 2 publications · 32 citations
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2 publications · 30 citations
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Repository · 2 publications · 1.2K citations
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