# Local Fisher Discriminant Analysis

Functions for performing and visualizing Local Fisher Discriminant Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised Local Fisher Discriminant Analysis(SELF).

R package for performing and visualizing Local Fisher Discriminant Analysis, Kernel Local Fisher Discriminant Analysis, and Semi-supervised Local Fisher Discriminant Analysis.

Introduction to the algorithms and their application can be found here and here. These methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

Welcome any feedback and pull request.

## Examples

### Local Fisher Discriminant Analysis(LFDA)

Suppose we want to reduce the dimensionality of the original data set (we are using `iris` data set here) to 3, then we can run the following:

### Kernel Local Fisher Discriminant Analysis(KLFDA)

The main usage is the same except for an additional `kmatrixGauss` call to the original data set to perform a kernel trick:

Note that the `predict` method for klfda is still under development. The `plot` method works the same way as in `lfda`.

### Semi-supervised Local Fisher Discriminant Analysis(SELF)

This algorithm requires one additional argument such as `beta` that represents the degree of semi-supervisedness. Let's assume we ignore 10% of the labels in `iris` data set:

The methods `predict` and `plot` work the same way as in `lfda`.

### Integration with {ggplot2::autoplot}

`{ggplot2::autoplot}` has been integrated with this package. Now `{lfda}` can be plotted in 2D easily and beautifully using `{ggfortify}` package. Go to this link and scroll down to the last section for an example.

# 作者： 唐源（Yuan Tang）

## 从Github上下载和安装最新的发展版本：

### 核局部Fisher判别分析 - Kernel Local Fisher Discriminant Analysis(KLFDA)

`klfda`的主要使用方法和`lfda`的使用方法基本一样，只是在使用lfda之前先要用`kmatrixGauss`对原数据进行核转换:

### 半监督局部Fisher判别分析- Semi-supervised Local Fisher Discriminant Analysis(SELF)

`predict``plot`的使用方法和`lfda`的完全一样

# Reference manual

install.packages("lfda")

1.1.3 by Yuan Tang, 2 years ago

https://github.com/terrytangyuan/lfda

Report a bug at https://github.com/terrytangyuan/lfda/issues

Browse source code at https://github.com/cran/lfda

Authors: Yuan Tang [aut, cre] , Wenxuan Li [ctb] , Nan Xiao [ctb, cph] , Zachary Deane-Mayer [ctb]

Documentation:   PDF Manual

Imports plyr, grDevices, rARPACK

Suggests testthat, rgl

Imported by dml.

Suggested by ggfortify.

See at CRAN