65 search results - page 5 / 13 » Is Nonparametric Learning Practical in Very High Dimensional... |
CVPR
2007
IEEE
14 years 9 months ago
2007
IEEE
Pattern variation is a major factor that affects the performance of recognition systems. In this paper, a novel manifold tangent modeling method called Discriminant Additive Tange...
JMLR
2010
13 years 2 months ago
2010
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
CVPR
2010
IEEE
14 years 3 months ago
2010
IEEE
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
ECML
2006
Springer
13 years 11 months ago
2006
Springer
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
ICANN
1997
Springer
13 years 11 months ago
1997
Springer
Abstract. In this paper we compare two methods for intrinsic dimensionality (ID) estimation based on optimally topology preserving maps (OTPMs). The rst one is a direct approach, w...