502 search results - page 29 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
IDEAL
2004
Springer
14 years 2 months ago
2004
Springer
A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We pro...
CCE
2006
13 years 8 months ago
2006
Principal differential analysis (PDA) is an alternative parameter estimation technique for differential equation models in which basis functions (e.g., B-splines) are fitted to dy...
NN
2000
Springer
13 years 8 months ago
2000
Springer
ICA (independent component analysis) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such ...
ICML
2007
IEEE
14 years 9 months ago
2007
IEEE
Eigenvalue problems are rampant in machine learning and statistics and appear in the context of classification, dimensionality reduction, etc. In this paper, we consider a cardina...
BMCBI
2006
13 years 8 months ago
2006
Background: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing ...