Title :
An Information Theoretic Perspective to Kernel K-Means
Author :
Jenssen, Robert ; Eltoft, Torbjorn
Author_Institution :
Dept. of Phys., Univ. of Tromso, Tromso
Abstract :
In this paper, we provide an information theoretic perspective to kernel K-means. We show that kernel K-means corresponds to maximizing an integrated squared error divergence measure between Parzen window estimated cluster probability density functions. Equivalently, this corresponds to a Bayes-like clustering rule in the input space, taking into account the Renyi entropies of the clusters.
Keywords :
Bayes methods; information theory; mean square error methods; pattern clustering; probability; Bayes-like clustering; Parzen window; Renyi entropy; cluster probability density function; information theory; kernel K-means; squared error divergence measure; Clustering algorithms; Density measurement; Entropy; Independent component analysis; Kernel; Principal component analysis; Probability density function; Signal processing algorithms; Support vector machines; Time measurement;
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2006.275541
