Bayesian K-Means as a "Maximization-Expectation" Algorithm
نویسندگان
چکیده
We introduce a new class of "maximization-expectation" (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical expectation-maximization algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data structures such as kd-trees and conga lines. The marginalization over parameters ensures that we retain the ability to infer model structure (i.e., number of clusters). As an important example, we discuss a top-down Bayesian k-means algorithm and a bottom-up agglomerative clustering algorithm. In experiments, we compare these algorithms against a number of alternative algorithms that have recently appeared in the literature.
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Bayesian K-Means as a “Maximization-Expectation” Algorithm
We introduce a new class of “maximization expectation” (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical EM algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data-structures such as kdtrees and cong...
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ورودعنوان ژورنال:
- Neural computation
دوره 21 4 شماره
صفحات -
تاریخ انتشار 2006