154 search results - page 10 / 31 » Incremental Nonlinear Dimensionality Reduction by Manifold L... |
119
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KDD
2007
ACM
16 years 1 days ago
2007
ACM
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
ICASSP
2008
IEEE
15 years 6 months ago
2008
IEEE
This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality...
100
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ICML
2004
IEEE
16 years 14 days ago
2004
IEEE
Many difficult visual perception problems, like 3D human motion estimation, can be formulated in terms of inference using complex generative models, defined over high-dimensional ...
109
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MM
2010
ACM
14 years 12 months ago
2010
ACM
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...
109
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ICML
2007
IEEE
16 years 14 days ago
2007
IEEE
Distance metric learning and nonlinear dimensionality reduction are two interesting and active topics in recent years. However, the connection between them is not thoroughly studi...