502 search results - page 14 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
CSDA
2008
13 years 7 months ago
2008
A computationally fast procedure for identifying outliers is presented, that is particularly effective in high dimensions. This algorithm utilizes simple properties of principal c...
HIS
2008
13 years 9 months ago
2008
This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is th...
IGARSS
2009
13 years 5 months ago
2009
Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remotesensing data. Features extracted using KPCA are used to construct the Ex...
IJON
2006
13 years 7 months ago
2006
In this paper, it is shown that Independent Component Analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise Principal Component Analysis (PCA). Consequently,...
BMVC
2010
13 years 5 months ago
2010
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...