Manifold Constrained Variational Mixtures | Zendy

Cédric Archambeau | Zendy; Michel Verleysen | Zendy

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Manifold Constrained Variational Mixtures

Author(s) -

Cédric Archambeau,

Michel Verleysen

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

DOI - 10.1007/11550907_44

Subject(s) - geodesic , dimension (graph theory) , computer science , manifold (fluid mechanics) , intrinsic dimension , euclidean distance , measure (data warehouse) , frame (networking) , algorithm , euclidean space , gaussian , euclidean geometry , artificial intelligence , mathematical optimization , mathematics , data mining , mathematical analysis , geometry , pure mathematics , curse of dimensionality , mechanical engineering , telecommunications , physics , quantum mechanics , engineering

In many data mining applications, the data manifold is of lower dimension than the dimension of the input space. In this paper, it is proposed to take advantage of this additional information in the frame of variational mixtures. The responsibilities computed in the VBE step are constrained according to a discrepancy measure between the Euclidean and the geodesic distance. The methodology is applied to variational Gaussian mixtures as a particular case and outperforms the standard approach, as well as Parzen windows, on both artificial and real data.

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