Subspace clustering, which aims at yielding a low-dimensional structure of high-dimensional data, is a fundamental clustering problem. Sparse subspace clustering (SSC) achieves state-of-the-art clustering performances by imposing sparse constraint on the coefficient matrix. However, most SSCs do not exploit the intrinsic relationship or prior information embedded in data. In this paper, we propose Laplacian embedded sparse subspace clustering, in which the intrinsic relationship of data is enforced by introducing a graph Laplacian regularization term into the clustering model. Then a symmetric constraint is imposed on the sparse representation to guarantee weight consistency for each pair of data points. To further offset the instability and control smoothness, a consistency penalty term is utilized to encourage the sequential property of data. Finally, the inexact augmented Lagrange multipliers (ALM) technique is adopted to solve the optimization problem. Experimental results on real-world data sets demonstrate the superior performance of the proposed algorithm over state-of-the-art methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.