NDLI: Margin-based active subspace clustering

Content Provider	IEEE Xplore Digital Library
Author	Lipor, J. Balzano, L.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Michigan, Ann Arbor, MI, USA (Lipor, J.; Balzano, L.)
Abstract	Subspace clustering has typically been approached as an unsupervised machine learning problem. However in several applications where the union of subspaces model is useful, it is also reasonable to assume you have access to a small number of labels. In this paper we investigate the benefit labeled data brings to the subspace clustering problem. We focus on incorporating labels into the k-subspaces algorithm, a simple and computationally efficient alternating estimation algorithm. We find that even a very small number of randomly selected labels can greatly improve accuracy over the unsupervised approach. We demonstrate that with enough labels, we get a significant improvement by using actively selected labels chosen for points that are nearly equidistant to more than one estimated subspace. We show this improvement on simulated data and face images.
Starting Page	377
Ending Page	380
File Size	312774
Page Count	4
File Format	PDF
e-ISBN	9781479919635
DOI	10.1109/CAMSAP.2015.7383815
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Computational modeling Conferences Clustering methods Clustering algorithms Data models Face
Content Type	Text
Resource Type	Article

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