The Informativeness of k-Means for Learning Mixture Models

10.1109/ISIT.2018.8437304

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Bibliographic Details
Main Authors:	Liu, Zhaoqiang, Tan, Vincent YF
Other Authors:	ELECTRICAL AND COMPUTER ENGINEERING
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:	Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Computer Science Engineering k-means algorithm Mixture models Fundamental limits Log-concave distribution Dimensionality reduction Principal component analysis Optimal clusterings ALGORITHMS
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/171683
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-171683
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spelling	sg-nus-scholar.10635-1716832024-04-25T03:37:39Z The Informativeness of k-Means for Learning Mixture Models Liu, Zhaoqiang Tan, Vincent YF ELECTRICAL AND COMPUTER ENGINEERING Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Computer Science Engineering k-means algorithm Mixture models Fundamental limits Log-concave distribution Dimensionality reduction Principal component analysis Optimal clusterings ALGORITHMS 10.1109/ISIT.2018.8437304 IEEE International Symposium on Information Theory - Proceedings 2018-June, 15 August 2018 2020-07-23T07:05:28Z 2020-07-23T07:05:28Z 2019-06-11 2020-07-22T19:27:02Z Article Liu, Zhaoqiang, Tan, Vincent YF (2019-06-11). The Informativeness of k-Means for Learning Mixture Models. IEEE International Symposium on Information Theory - Proceedings 2018-June, 15 August 2018. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIT.2018.8437304 9781538647806 21578095 https://scholarbank.nus.edu.sg/handle/10635/171683 en Institute of Electrical and Electronics Engineers Inc. Elements
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
language	English
topic	Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Computer Science Engineering k-means algorithm Mixture models Fundamental limits Log-concave distribution Dimensionality reduction Principal component analysis Optimal clusterings ALGORITHMS
spellingShingle	Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Computer Science Engineering k-means algorithm Mixture models Fundamental limits Log-concave distribution Dimensionality reduction Principal component analysis Optimal clusterings ALGORITHMS Liu, Zhaoqiang Tan, Vincent YF The Informativeness of k-Means for Learning Mixture Models
description	10.1109/ISIT.2018.8437304
author2	ELECTRICAL AND COMPUTER ENGINEERING
author_facet	ELECTRICAL AND COMPUTER ENGINEERING Liu, Zhaoqiang Tan, Vincent YF
format	Article
author	Liu, Zhaoqiang Tan, Vincent YF
author_sort	Liu, Zhaoqiang
title	The Informativeness of k-Means for Learning Mixture Models
title_short	The Informativeness of k-Means for Learning Mixture Models
title_full	The Informativeness of k-Means for Learning Mixture Models
title_fullStr	The Informativeness of k-Means for Learning Mixture Models
title_full_unstemmed	The Informativeness of k-Means for Learning Mixture Models
title_sort	informativeness of k-means for learning mixture models
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2020
url	https://scholarbank.nus.edu.sg/handle/10635/171683
_version_	1800914127830384640

The Informativeness of k-Means for Learning Mixture Models

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