Constrained spectral clustering under a local proximity structure assumption

Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence

Volumn , Issue , 2005, Pages 866-867

  Xu, Qianjun ^a   Desjardins, Marie ^a   Wagstaff, Kiri ^b  

^a UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT ACQUISITION; PARAMETER SELECTION; SPECTRAL CLUSTERING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA STRUCTURES; HEURISTIC METHODS; PARAMETER ESTIMATION;

CONSTRAINT THEORY;

EID: 32844465464     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

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2. Kamvar, S. D.; Klein, D.; and Manning, C. D. 2003. Spectral learning. In Proceedings of IJCAI-03, 561-566.
3. Klein, D.; Kamvar, S. D.; and Manning, C. D. 2002. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In Proceedings of ICML-02, 307-314. Morgan Kaufmann.
4. Meilǎ, M., and Shi, J. 2001. A random walks view of spectral segmentation. In Proceedings of AI-STAT'01.
5. Rand, W. 1971. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66:846-850.
6. Yu, S. X., and Shi, J. 2004. Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2): 173-183.