A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction

Neil D. Lawrence
, 2010.

Abstract

We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting probabilistic models are based on GRFs. The resulting model is a nonlinear generalization of principal component analysis. We show that parameter fitting in the locally linear embedding is approximate maximum likelihood in these models. We develop new algorithms that directly maximize the likelihood and show that these new algorithms are competitive with the leading spectral approaches on a robot navigation visualization and a human motion capture data set. Finally the maximum likelihood perspective allows us to introduce a new approach to dimensionality reduction based on L1 regularization of the Gaussian random field via the graphical lasso.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-unifying10, title = {A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction}, author = {Neil D. Lawrence}, year = {}, editor = {}, url = {http://inverseprobability.com/publications/lawrence-unifying10.html}, abstract = {We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting probabilistic models are based on GRFs. The resulting model is a nonlinear generalization of principal component analysis. We show that parameter fitting in the locally linear embedding is approximate maximum likelihood in these models. We develop new algorithms that directly maximize the likelihood and show that these new algorithms are competitive with the leading spectral approaches on a robot navigation visualization and a human motion capture data set. Finally the maximum likelihood perspective allows us to introduce a new approach to dimensionality reduction based on L1 regularization of the Gaussian random field via the graphical lasso.} }
Endnote
%0 Conference Paper %T A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-unifying10 %I PMLR %J Proceedings of Machine Learning Research %P -- %U http://inverseprobability.com %V %W PMLR %X We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting probabilistic models are based on GRFs. The resulting model is a nonlinear generalization of principal component analysis. We show that parameter fitting in the locally linear embedding is approximate maximum likelihood in these models. We develop new algorithms that directly maximize the likelihood and show that these new algorithms are competitive with the leading spectral approaches on a robot navigation visualization and a human motion capture data set. Finally the maximum likelihood perspective allows us to introduce a new approach to dimensionality reduction based on L1 regularization of the Gaussian random field via the graphical lasso.
RIS
TY - CPAPER TI - A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-lawrence-unifying10 PB - PMLR SP - DP - PMLR EP - L1 - UR - http://inverseprobability.com/publications/lawrence-unifying10.html AB - We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting probabilistic models are based on GRFs. The resulting model is a nonlinear generalization of principal component analysis. We show that parameter fitting in the locally linear embedding is approximate maximum likelihood in these models. We develop new algorithms that directly maximize the likelihood and show that these new algorithms are competitive with the leading spectral approaches on a robot navigation visualization and a human motion capture data set. Finally the maximum likelihood perspective allows us to introduce a new approach to dimensionality reduction based on L1 regularization of the Gaussian random field via the graphical lasso. ER -
APA
Lawrence, N.D.. (). A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction. , in PMLR :-

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