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78
Voted
ICML
2004
IEEE
15 years 11 months ago
2004
IEEE
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
74
Voted
ESANN
2007
14 years 11 months ago
2007
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
IEICET
2010
14 years 8 months ago
2010
Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solvi...
ICASSP
2010
IEEE
14 years 10 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....
101
click to vote
BMCBI
2010
14 years 9 months ago
2010
Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...