ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theoretic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information cut for clustering using a gradient descent approach
Pattern Recognition
Sharing training data among different activity classes
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Hi-index | 0.00 |
This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data patterns, in order to maximize the recent Cauchy-Schwarz (CS) probability density function (pdf) distance measure. Each pdf corresponds to a cluster. The CS distance is estimated analytically and non-parametrically by means of the Parzen window technique for density estimation. The resulting form of the cost function makes it possible to develop an efficient adaption procedure based on constrained gradient descent, using stochastic approximation of the gradients. The computational complexity of the algorithm is O(MN), M ≪ N, where N is the total number of data patterns and M is the number of data patterns used in the stochastic approximation. We show that the new algorithm is capable of performing well on several odd-shaped and irregular data sets.