A free energy based approach for distance metric learning

Sho Inaba; Rahul V. Kulkarni; Carl T. Fakhry; Kourosh Zarringhalam

doi:10.1145/3292500.3330975

A free energy based approach for distance metric learning

Sho Inaba
, Rahul V. Kulkarni
, Carl T. Fakhry
, Kourosh Zarringhalam

University of Massachusetts Boston

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	5-13
Number of pages	9
ISBN (Electronic)	9781450362016
DOIs	https://doi.org/10.1145/3292500.3330975
State	Published - Jul 25 2019
Event	25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States Duration: Aug 4 2019 → Aug 8 2019

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Country/Territory	United States
City	Anchorage
Period	8/4/19 → 8/8/19

ASJC Scopus Subject Areas

Software
Information Systems

Keywords

Dimensionality reduction
Distance metric learning
High-dimensional data visualization

Access to Document

10.1145/3292500.3330975

Cite this

Inaba, S., Kulkarni, R. V., Fakhry, C. T., & Zarringhalam, K. (2019). A free energy based approach for distance metric learning. In KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 5-13). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3292500.3330975

A free energy based approach for distance metric learning. / Inaba, Sho; Kulkarni, Rahul V.; Fakhry, Carl T. et al.
KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2019. p. 5-13 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Inaba, S, Kulkarni, RV, Fakhry, CT & Zarringhalam, K 2019, A free energy based approach for distance metric learning. in KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 5-13, 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, Anchorage, United States, 8/4/19. https://doi.org/10.1145/3292500.3330975

Inaba S, Kulkarni RV, Fakhry CT, Zarringhalam K. A free energy based approach for distance metric learning. In KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2019. p. 5-13. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3292500.3330975

Inaba, Sho ; Kulkarni, Rahul V. ; Fakhry, Carl T. et al. / A free energy based approach for distance metric learning. KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2019. pp. 5-13 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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title = "A free energy based approach for distance metric learning",

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author = "Sho Inaba and Kulkarni, \{Rahul V.\} and Fakhry, \{Carl T.\} and Kourosh Zarringhalam",

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BT - KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

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ER -

A free energy based approach for distance metric learning

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this