70 search results - page 9 / 14 » Multiple Kernel Learning for Dimensionality Reduction |
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
PR
2007
13 years 7 months ago
2007
Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional s...
ICCV
2007
IEEE
14 years 1 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...
ICPR
2008
IEEE
14 years 8 months ago
2008
IEEE
Multiple observation improves the performance of 3D object classification. However, since the distribution of feature vectors obtained from multiple view points have strong nonlin...
GRC
2010
IEEE
13 years 8 months ago
2010
IEEE
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...