502 search results - page 24 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
JCIT
2010
13 years 3 months ago
2010
With the development of telecom business, customer churn prediction becomes more and more important. An outstanding issue in customer churn prediction is high dimensional problem....
NIPS
2007
13 years 10 months ago
2007
We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse line...
ACMACE
2008
ACM
13 years 10 months ago
2008
ACM
Dimensionality reduction is a statistical tool commonly used to map high-dimensional data into lower a dimensionality. The transformed data is typically more suitable for regressi...
ICIP
2002
IEEE
14 years 10 months ago
2002
IEEE
We introduce an efficient statistical modeling technique called Mixture of Principal Components (MPC). This model is a linear extension to the traditional Principal Component Anal...
ISBI
2007
IEEE
14 years 3 months ago
2007
IEEE
Statistical shape analysis techniques commonly employed in the medical imaging community, such as Active Shape Models or Active Appearance Models, rely on Principal Component Anal...