91 search results - page 11 / 19 » Kernel Dimensionality Reduction for Supervised Learning |
90
Voted
KDD
2003
ACM
16 years 24 days ago
2003
ACM
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...
102
Voted
ICML
2009
IEEE
16 years 1 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...
139
Voted
CVPR
2011
IEEE
14 years 7 months ago
2011
IEEE
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
113
Voted
ICMCS
2006
IEEE
15 years 6 months ago
2006
IEEE
It is well-known that supervised learning techniques such as linear discriminant analysis (LDA) often suffer from the so called small sample size problem when apply to solve face ...
105
Voted
CVPR
2007
IEEE
16 years 2 months ago
2007
IEEE
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...