52 search results - page 3 / 11 » Nonlinear kernel density principal component analysis with a... |
IDEAL
2003
Springer
13 years 10 months ago
2003
Springer
Abstract. Multidimensional data projection and visualisation are becoming increasingly important and have found wide applications in many fields such as decision support, bioinform...
TIP
2011
13 years 13 days ago
2011
—This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formu...
ICPR
2006
IEEE
14 years 6 months ago
2006
IEEE
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...
ICASSP
2010
IEEE
13 years 5 months ago
2010
IEEE
The importance estimation problem (estimating the ratio of two probability density functions) has recently gathered a great deal of attention for use in various applications, e.g....
WSCG
2004
13 years 6 months ago
2004
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...