Construction of a reliable similarity matrix is fundamental for graph-based clustering methods. However, most of the current work is built upon some simple manifold structure, whereas limited work has been conducted on nonlinear data sets where data reside in a union of manifolds rather than a union of subspaces. Therefore, we construct a similarity graph to capture both global and local manifold structures of the input data set. The global structure is exploited based on the self-expressive property of data in an implicit feature space using kernel methods. Since the similarity graph computation is independent of the subsequent clustering, the final results may be far from optimal. To overcome this limitation, we simultaneously learn similarity graph and clustering structure in a principled way. Experimental studies demonstrate that our proposed algorithms deliver consistently superior results to other state-of-The-Art algorithms.
|Title of host publication||Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017|
|Number of pages||4|
|State||Published - May 16 2017|
|Event||33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States|
Duration: Apr 19 2017 → Apr 22 2017
|Name||Proceedings - International Conference on Data Engineering|
|Conference||33rd IEEE International Conference on Data Engineering, ICDE 2017|
|Period||4/19/17 → 4/22/17|
Bibliographical noteFunding Information:
This work is supported by the U.S. National Science Foundation under Grant IIS 1218712. The corresponding author is Qiang Cheng.
© 2017 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Information Systems