910 search results - page 12 / 182 » Progressive Principal Component Analysis |
192
Voted
ICML
2004
IEEE
16 years 2 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
127
click to vote
CORR
2007
Springer
15 years 1 months ago
2007
Springer
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
109
click to vote
ISQED
2000
IEEE
15 years 6 months ago
2000
IEEE
A new algorithm to determine the number and value of realistic worst-case models for the performance of module library components is presented in this paper. The proposed algorith...
179
Voted
BMCBI
2011
14 years 5 months ago
2011
Background: The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used ...
112
click to vote
ICML
2006
IEEE
16 years 2 months ago
2006
IEEE
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...