71 search results - page 4 / 15 » Learning low dimensional predictive representations |
ICCV
2009
IEEE
15 years 14 days ago
2009
IEEE
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
PSIVT
2009
Springer
14 years 2 months ago
2009
Springer
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
NIPS
2004
13 years 8 months ago
2004
Statistical language models estimate the probability of a word occurring in a given context. The most common language models rely on a discrete enumeration of predictive contexts ...
CVPR
2003
IEEE
14 years 9 months ago
2003
IEEE
In this paper, a novel method to learn the intrinsic object structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a...
CVPR
2010
IEEE
14 years 3 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...