AI Seminar

Learning Nonparametric Kernel Matrices from Pairwise Constraints

Rong Jin

Associate Professor
Department of Computer Science and Engineering, Michigan State University
Tuesday, October 14, 2008
4:00pm - 5:30pm
Stained-Glass Conference Room (3725 Beyster Bldg.)

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About the Event

Kernel plays an important role in many machine learning techniques. Many kernel learning algorithms (e.g., multiple kernel learning) have to assume parametric forms for the target kernel functions, which can significantly limit the capability of kernels in fitting diverse patterns of data. In this paper, we present a framework for non-parametric kernel learning that learns a kernel matrix from a given set of pairwise constraints. A graph Laplacian of the observed data is introduced as a regularizer when optimizing the kernel matrix. An efficient algorithm is developed to solve the related Semi-Definite Programming (SDP) problem. We also present an active learning method for the proposed framework for non-parametric kernel learning. Extensive evaluation on clustering with a number of UCI datasets shows that the proposed method is more effective than other state-of-the-art techniques for kernel learning.

Additional Information

Sponsor(s): Toyota AI Seminar Series

Open to: Public