91 search results - page 16 / 19 » Kernel Dimensionality Reduction for Supervised Learning |
79
Voted
ISDA
2009
IEEE
15 years 4 months ago
2009
IEEE
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors t...
ICML
2006
IEEE
15 years 11 months ago
2006
IEEE
Dimensionality reduction is an important pre-processing step for many applications. Linear Discriminant Analysis (LDA) is one of the well known methods for supervised dimensionali...
143
Voted
CVPR
2012
IEEE
13 years 21 days ago
2012
IEEE
In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor w...
122
Voted
TSMC
2010
14 years 5 months ago
2010
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
86
Voted
ICCV
2009
IEEE
16 years 3 months ago
2009
IEEE
We propose an unconventional but highly effective approach
to robust fitting of multiple structures by using statistical
learning concepts. We design a novel Mercer kernel
for t...