454 search results - page 10 / 91 » Principal Component Analysis Based on L1-Norm Maximization |
CORR
2010
Springer
13 years 7 months ago
2010
Springer
Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like ...
JMLR
2010
13 years 2 months ago
2010
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
ECML
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
NIPS
2008
13 years 9 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
ICPR
2000
IEEE
14 years 8 months ago
2000
IEEE
This paper describes a new method to recover the sign of the local Gaussian curvature at each point on the visible surface of a 3-D object. Multiple (p > 3) shaded images are a...