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- Kernel Statistics toolbox
- Last Update:2006/10/29
- For questions or comments, please email
Yuh-Jye Lee, yuh-jye@mail.ntust.edu.tw or
Yi-Ren Yeh, yeh@stat.sinica.edu.tw or
Su-Yun Huang, syhuang@stat.sinica.edu.tw
- Web site: http://dmlab1.csie.ntust.edu.tw/downloads/
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Table of Contents
=================
- Introduction
- Key Features
- Data Format
For classification
For regression
- Code Usage with Examples
KDR
UseKDR
KPCA
KSIR
- Dimension Reduction Using KPCA or KSIR with Examples
KPCA procedure
KSIR procedure for classification
KSIR procedure for regression
- License
Introduction
============
Kernel Statistics toolbox is still in development. Two algorithms are now
available. One is kernel principal component analysis (KPCA). The other
is kernel sliced inverse regression (KSIR).
Key Features
============
* Construct principal components in the input space and feature space.
* Provide a preprocess for preventing ill-posed problem encountered in KSIR.
* Support linear, polynomial and radial basis kernels.
* Can handle large scale problems by using reduced kernel.
Data Format
===========
Kernel Statistics toolbox is implemented in Matlab. Use a data format which
can be loaded into Matlab. The instances are represented by a matrix (rows
for instances and columns for variables) and the labels (1,2,...,k) or
responses are represented by a column vector. Note that you also can
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