39 search results - page 4 / 8 » Covariance Regularization for Supervised Learning in High Di... |
TIP
2008
13 years 9 months ago
2008
In this paper, we present an automatic classification framework combining appearance based features and Hidden Markov Models (HMM) to detect unusual events in image sequences. One...
PAMI
2010
13 years 4 months ago
2010
Abstract--This paper presents local spline regression for semisupervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the...
CORR
2007
Springer
13 years 9 months ago
2007
Springer
We consider the least-square regression problem with regularization by a block 1-norm, that is, a sum of Euclidean norms over spaces of dimensions larger than one. This problem, r...
CVPR
2007
IEEE
14 years 11 months ago
2007
IEEE
The success of tensor-based subspace learning depends heavily on reducing correlations along the column vectors of the mode-k flattened matrix. In this work, we study the problem ...
PSIVT
2009
Springer
14 years 4 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...