VDM Verlag
Dimensionality Reduction for Classification with High-Dimensional Data
Product Code:
9783639288681
ISBN13:
9783639288681
Condition:
New
$63.72
$63.18
Sale 1%
Dimensionality Reduction for Classification with High-Dimensional Data
$63.72
$63.18
Sale 1%
High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies.
| Author: Siva Tian |
| Publisher: VDM Verlag |
| Publication Date: Aug 25, 2010 |
| Number of Pages: 124 pages |
| Binding: Paperback or Softback |
| ISBN-10: 3639288688 |
| ISBN-13: 9783639288681 |
Related Products
Sale 1%
Sale 2%
