Support Vector Data Descriptions and k-Means Clustering: One Class?
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Goernitz, Nico | - |
dc.contributor.author | Lima, Luiz Alberto | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Kloft, Marius | - |
dc.contributor.author | Nakajima, Shinichi | - |
dc.date.accessioned | 2021-09-02T07:26:26Z | - |
dc.date.available | 2021-09-02T07:26:26Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/73649 | - |
dc.description.abstract | We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a PYTHON software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | KERNEL | - |
dc.subject | SVMS | - |
dc.title | Support Vector Data Descriptions and k-Means Clustering: One Class? | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1109/TNNLS.2017.2737941 | - |
dc.identifier.scopusid | 2-s2.0-85030751821 | - |
dc.identifier.wosid | 000443083700006 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.29, no.9, pp.3994 - 4006 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.volume | 29 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 3994 | - |
dc.citation.endPage | 4006 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | KERNEL | - |
dc.subject.keywordPlus | SVMS | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | k-means | - |
dc.subject.keywordAuthor | one-class classification | - |
dc.subject.keywordAuthor | support vector data description (SVDD) | - |
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