Open AccessProceedings Article
Convergence Properties of the K-Means Algorithms
Léon Bottou,Yoshua Bengio +1 more
- Vol. 7, pp 585-592
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TLDR
It is shown that the K-Means algorithm actually minimizes the quantization error using the very fast Newton algorithm.Abstract:
This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-Means algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the EM algorithm to this hard threshold case. We show that the K-Means algorithm actually minimizes the quantization error using the very fast Newton algorithm.read more
Citations
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References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Proceedings Article
Note on Learning Rate Schedules for Stochastic Optimization
Christian J. Darken,John Moody +1 more
TL;DR: "search-then-converge" type schedules which outperform the classical constant and "running average" (1/t) schedules both in speed of convergence and quality of solution.