Abstract
Current well-known data description method such as Support Vector Data Description is conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Deterministic Annealing Multi-sphere Support Vector Data Description (DAMS-SVDD) approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.
Original language | English |
---|---|
Title of host publication | International Conference on Neural Information Processing |
Subtitle of host publication | Lecture Notes in Computer Science |
Editors | Tingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung |
Place of Publication | Berlin Heidellberg, Germany |
Publisher | Springer |
Pages | 183-190 |
Number of pages | 8 |
Volume | 7665 |
ISBN (Print) | 9783642344879 |
DOIs | |
Publication status | Published - 2012 |
Event | 19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012 |
Conference
Conference | 19th International Conference on Neural Information Processing 2012 |
---|---|
Country/Territory | Qatar |
City | Doha |
Period | 12/11/12 → 15/11/12 |