메뉴 건너뛰기




Volumn 15, Issue , 2011, Pages 552-560

Dimensionality reduction for spectral clustering

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION AREA; COMPUTATIONAL VISION; DATA TYPE; DIMENSIONALITY REDUCTION; PROJECTION OPERATOR; SPECTRAL CLUSTERING;

EID: 84862271729     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (47)

References (30)
  • 2
    • 33749317042 scopus 로고    scopus 로고
    • Learning spectral clustering, with application to speech separation
    • F. R. Bach and M. I. Jordan. Learning spectral clustering, with application to speech separation. Journal of Machine Learning Research, 7:1963-2001, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1963-2001
    • Bach, F.R.1    Jordan, M.I.2
  • 4
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 15
    • 26444566340 scopus 로고    scopus 로고
    • Contour regression: A general approach to dimension reduction
    • B. Li, H. Zha, and F. Chiaramonte. Contour regression: A general approach to dimension reduction. The Annals of Statistics, 33:1580-1616, 2005.
    • (2005) The Annals of Statistics , vol.33 , pp. 1580-1616
    • Li, B.1    Zha, H.2    Chiaramonte, F.3
  • 17
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • U. V. Luxburg. A tutorial on spectral clustering. Statistics and Computing, 5:395-416, 2007.
    • (2007) Statistics and Computing , vol.5 , pp. 395-416
    • Luxburg, U.V.1
  • 23
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.1    Saul, L.2
  • 26
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles-a knowledge reuse framework for combining multiple partitions
    • A. Strehl and J. Ghosh. Cluster ensembles-a knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research, 3:583-617, 2002.
    • (2002) Journal on Machine Learning Research , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 27
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • J. B. Tenenbaum, V. Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    Silva, V.2    Langford, J.C.3
  • 29
    • 0034800371 scopus 로고    scopus 로고
    • Principal component analysis for clustering gene expression data
    • K. Yeung and W. Ruzzo. Principal component analysis for clustering gene expression data. Bioinformatics, 17:763-774, 2001.
    • (2001) Bioinformatics , vol.17 , pp. 763-774
    • Yeung, K.1    Ruzzo, W.2
  • 30
    • 59349084916 scopus 로고    scopus 로고
    • Multiway spectral clustering: A margin-based perspective
    • Z. Zhang and M. I. Jordan. Multiway spectral clustering: A margin-based perspective. Statistical Science, 23:383-403, 2008.
    • (2008) Statistical Science , vol.23 , pp. 383-403
    • Zhang, Z.1    Jordan, M.I.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.