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» A Hilbert Space Embedding for Distributions
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ICML
2010
IEEE
13 years 4 months ago
Hilbert Space Embeddings of Hidden Markov Models
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
Le Song, Sajid M. Siddiqi, Geoffrey J. Gordon, Ale...
JMLR
2010
146views more  JMLR 2010»
12 years 10 months ago
Nonparametric Tree Graphical Models
We introduce a nonparametric representation for graphical model on trees which expresses marginals as Hilbert space embeddings and conditionals as embedding operators. This formul...
Le Song, Arthur Gretton, Carlos Guestrin
ICCV
2005
IEEE
14 years 5 months ago
Neighborhood Preserving Embedding
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
Xiaofei He, Deng Cai, Shuicheng Yan, HongJiang Zha...
KI
2009
Springer
13 years 10 months ago
Generalized Clustering via Kernel Embeddings
Abstract. We generalize traditional goals of clustering towards distinguishing components in a non-parametric mixture model. The clusters are not necessarily based on point locatio...
Stefanie Jegelka, Arthur Gretton, Bernhard Sch&oum...
NIPS
2008
13 years 5 months ago
Characteristic Kernels on Groups and Semigroups
Embeddings of random variables in reproducing kernel Hilbert spaces (RKHSs) may be used to conduct statistical inference based on higher order moments. For sufficiently rich (char...
Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur G...