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» Combining Variable Selection with Dimensionality Reduction
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CVPR
2005
IEEE
14 years 6 months ago
Combining Variable Selection with Dimensionality Reduction
Lior Wolf, Stanley M. Bileschi
JMLR
2010
155views more  JMLR 2010»
12 years 11 months ago
Bayesian Gaussian Process Latent Variable Model
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
Michalis Titsias, Neil D. Lawrence
SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
14 years 4 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
PAMI
2007
102views more  PAMI 2007»
13 years 4 months ago
Feature Subset Selection and Ranking for Data Dimensionality Reduction
—A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one ...
Hua-Liang Wei, Stephen A. Billings
ICCV
2007
IEEE
14 years 6 months ago
Real-time Body Tracking Using a Gaussian Process Latent Variable Model
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...
Shaobo Hou, Aphrodite Galata, Fabrice Caillette, N...