Subspace Clustering Via Optimal Direction Search

Keywords

Convex optimization; face clustering; innovation pursuit; spectral clustering; unsupervised learning

Abstract

This letter presents a new spectral-clustering-based approach to the subspace clustering problem. Underpinning the proposed method is a convex program for optimal direction search, which for each data point d finds an optimal direction in the span of the data that has minimum projection on the other data points and nonvanishing projection on d. The obtained directions are subsequently leveraged to identify a neighborhood set for each data point. An alternating direction method of multipliers framework is provided to efficiently solve for the optimal directions. The proposed method is shown to often outperform the existing subspace clustering methods, particularly for unwieldy scenarios involving high levels of noise and close subspaces, and yields the state-of-the-art results for the problem of face clustering using subspace segmentation.

Publication Date

12-1-2017

Publication Title

IEEE Signal Processing Letters

Volume

24

Issue

12

Number of Pages

1793-1797

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/LSP.2017.2757901

Socpus ID

85030787721 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85030787721

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