Deterministic Annealing Multi-sphere Support Vector Data Description

Trung Le; Dat Tran; Wanli Ma; Dharmendra Sharma

doi:10.1007/978-3-642-34487-9_23

Deterministic Annealing Multi-sphere Support Vector Data Description

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

1 Citation (Scopus)

3 Downloads (Pure)

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	https://doi.org/10.1007/978-3-642-34487-9_23
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

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10.1007/978-3-642-34487-9_23

Cite this

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title = "Deterministic Annealing Multi-sphere Support Vector Data Description",

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.",

author = "Trung Le and Dat Tran and Wanli Ma and Dharmendra Sharma",

year = "2012",

doi = "10.1007/978-3-642-34487-9_23",

language = "English",

isbn = "9783642344879",

volume = "7665",

pages = "183--190",

editor = "Tingwen Huang and Zhigang Zeng and Chuandong Li and Leung, {Chi Sing}",

booktitle = "International Conference on Neural Information Processing",

publisher = "Springer",

address = "Netherlands",

note = "19th International Conference on Neural Information Processing 2012 ; Conference date: 12-11-2012 Through 15-11-2012",

}

Le, T, Tran, D, Ma, W & Sharma, D 2012, Deterministic Annealing Multi-sphere Support Vector Data Description. in T Huang, Z Zeng, C Li & CS Leung (eds), International Conference on Neural Information Processing: Lecture Notes in Computer Science. vol. 7665, Springer, Berlin Heidellberg, Germany, pp. 183-190, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34487-9_23

Deterministic Annealing Multi-sphere Support Vector Data Description. / Le, Trung; Tran, Dat; Ma, Wanli et al.
International Conference on Neural Information Processing: Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. 7665 Berlin Heidellberg, Germany: Springer, 2012. p. 183-190.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Deterministic Annealing Multi-sphere Support Vector Data Description

AU - Le, Trung

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-34487-9_23

DO - 10.1007/978-3-642-34487-9_23

M3 - Conference contribution

SN - 9783642344879

VL - 7665

SP - 183

EP - 190

BT - International Conference on Neural Information Processing

A2 - Huang, Tingwen

A2 - Zeng, Zhigang

A2 - Li, Chuandong

A2 - Leung, Chi Sing

PB - Springer

CY - Berlin Heidellberg, Germany

T2 - 19th International Conference on Neural Information Processing 2012

Y2 - 12 November 2012 through 15 November 2012

ER -

Deterministic Annealing Multi-sphere Support Vector Data Description

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