File(s) under permanent embargo
Fast one-class support vector machine for novelty detection
chapter
posted on 2015-01-01, 00:00 authored by Trung Minh Le, Quoc-Dinh Phung, K Nguyen, Svetha VenkateshSvetha VenkateshNovelty detection arises as an important learning task in several applications. Kernel-based approach to novelty detection has been widely used due to its theoretical rigor and elegance of geometric interpretation. However, computational complexity is a major obstacle in this approach. In this paper, leveraging on the cutting-plane framework with the well-known One-Class Support Vector Machine, we present a new solution that can scale up seamlessly with data. The first solution is exact and linear when viewed through the cutting-plane; the second employed a sampling strategy that remarkably has a constant computational complexity defined relatively to the probability of approximation accuracy. Several datasets are benchmarked to demonstrate the credibility of our framework.
History
Event
Pacific-Asia Conference on Knowledge Discovery and Data MiningTitle of book
Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part IVolume
9078Series
Lecture notes in computer science; v.9078Chapter number
15Pagination
189 - 200Publisher
SpringerLocation
VietnamPlace of publication
Berlin, GermanyPublisher DOI
Start date
2015-01-01End date
2015-01-01ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319180328Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2015, SpringerExtent
59Editor/Contributor(s)
T Cao, E Lim, Z Zhou, T Ho, D Cheung, H MotodaUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC