Feature selection and kernel learning for local learning-based clustering

Hong Zeng*, Yiu Ming Cheung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

212 Citations (Scopus)


The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schlkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Original languageEnglish
Article number5661784
Pages (from-to)1532-1547
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
Publication statusPublished - Aug 2011

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • feature selection
  • High-dimensional data
  • kernel learning
  • local learning-based clustering
  • sparse weighting


Dive into the research topics of 'Feature selection and kernel learning for local learning-based clustering'. Together they form a unique fingerprint.

Cite this