CRAFT : ClusteR-specific Assorted Feature selecTion

In this thesis, we present a hierarchical Bayesian framework for clustering with cluster-specific feature selection. We derive a simplified model, CRAFT, by analyzing the asymptotic behavior of the log posterior formulations in a nonparametric MAP-based clustering setting in this framework. The model handles assorted data, i.e., both numeric and categorical data, and the underlying objective functions are intuitively appealing. The resulting algorithm is simple to implement and scales nicely, requires minimal parameter tuning, obviates the need to specify the number of clusters a priori, and compares favorably with other state-of-the-art methods on several datasets. We provide empirical evidence on carefully designed synthetic data sets to highlight the robustness of the algorithm to recover the underlying feature subspaces, even when the average dimensionality of the features across clusters is misspecified. Besides, the framework seamlessly allows for multiple views of clustering by interpolating between the two extremes of cluster-specific feature selection and global selection, and recovers the DP-means objective [14] under the degenerate setting of clustering without feature selection.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Garg, Vikas, Ph. D. (Vikas Kamur). Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Tommi S. Jaakkola and Cynthia Rudin.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/105697

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/105697