24 search results - page 2 / 5 » A kernel view of the dimensionality reduction of manifolds |
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
NIPS
2001
13 years 6 months ago
2001
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
ICCV
2007
IEEE
13 years 11 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
CVPR
2008
IEEE
14 years 7 months ago
2008
IEEE
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
CVPR
2010
IEEE
14 years 1 months ago
2010
IEEE
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...