Spectral kernels for classification

Authors:
Wenyuan Li;Kok-Leong Ong;Wee-Keong Ng;Aixin Sun
Affiliations:
Centre for Advanced Information Systems, Nanyang Technological University, Singapore;School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia;Centre for Advanced Information Systems, Nanyang Technological University, Singapore;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
Venue:
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Year:
2005

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Abstract

Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy.