Sparse optimal discriminant clustering

Yanhong Wang; Yixin Fang; Junhui Wang

doi:10.1007/s11222-015-9547-8

Sparse optimal discriminant clustering

Yanhong Wang, Yixin Fang, Junhui Wang

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

In this manuscript, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai in Adv Neural Inf Process Syst 23(12):2241–2249, 2009), and propose to use cross-validation to select the tuning parameter. Furthermore, because in high-dimensional data many of the features may be non-informative for clustering, we develop a variation of ODC, sparse optimal discriminant clustering (SODC), by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis.

Original language	English (US)
Pages (from-to)	629-639
Number of pages	11
Journal	Statistics and Computing
Volume	26
Issue number	3
DOIs	https://doi.org/10.1007/s11222-015-9547-8
State	Published - May 1 2016
Externally published	Yes

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Statistics and Probability
Statistics, Probability and Uncertainty
Computational Theory and Mathematics

Keywords

Cluster analysis
Cross-validation
High-dimensional data
Optimal score
Principal components analysis
Tuning parameter
Variable selection

Access to Document

10.1007/s11222-015-9547-8

Cite this

@article{065fd384cacd4e30b65bef1ab0c40a9f,

title = "Sparse optimal discriminant clustering",

abstract = "In this manuscript, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai in Adv Neural Inf Process Syst 23(12):2241–2249, 2009), and propose to use cross-validation to select the tuning parameter. Furthermore, because in high-dimensional data many of the features may be non-informative for clustering, we develop a variation of ODC, sparse optimal discriminant clustering (SODC), by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis.",

keywords = "Cluster analysis, Cross-validation, High-dimensional data, Optimal score, Principal components analysis, Tuning parameter, Variable selection",

author = "Yanhong Wang and Yixin Fang and Junhui Wang",

note = "Publisher Copyright: {\textcopyright} 2015, Springer Science+Business Media New York.",

year = "2016",

month = may,

day = "1",

doi = "10.1007/s11222-015-9547-8",

language = "English (US)",

volume = "26",

pages = "629--639",

journal = "Statistics and Computing",

issn = "0960-3174",

publisher = "Springer Netherlands",

number = "3",

}

TY - JOUR

T1 - Sparse optimal discriminant clustering

AU - Wang, Yanhong

AU - Fang, Yixin

AU - Wang, Junhui

PY - 2016/5/1

Y1 - 2016/5/1

N2 - In this manuscript, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai in Adv Neural Inf Process Syst 23(12):2241–2249, 2009), and propose to use cross-validation to select the tuning parameter. Furthermore, because in high-dimensional data many of the features may be non-informative for clustering, we develop a variation of ODC, sparse optimal discriminant clustering (SODC), by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis.

AB - In this manuscript, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai in Adv Neural Inf Process Syst 23(12):2241–2249, 2009), and propose to use cross-validation to select the tuning parameter. Furthermore, because in high-dimensional data many of the features may be non-informative for clustering, we develop a variation of ODC, sparse optimal discriminant clustering (SODC), by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis.

KW - Cluster analysis

KW - Cross-validation

KW - High-dimensional data

KW - Optimal score

KW - Principal components analysis

KW - Tuning parameter

KW - Variable selection

UR - http://www.scopus.com/inward/record.url?scp=84923008955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923008955&partnerID=8YFLogxK

U2 - 10.1007/s11222-015-9547-8

DO - 10.1007/s11222-015-9547-8

M3 - Article

AN - SCOPUS:84923008955

SN - 0960-3174

VL - 26

SP - 629

EP - 639

JO - Statistics and Computing

JF - Statistics and Computing

IS - 3

ER -

Sparse optimal discriminant clustering

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this