Gupta Potential

Matlab codes for principal component accumulation (PCAcc) method

Please CLICK HERE to download the Matlab scripts.

PCAcc method focuses on improving class separability by using the information

contained in multiple PC subspaces to identify different types of cancers.

The programs for the method are implemented in MATLAB. MATLAB 6.5 or higher

versions should be installed for runing these programs.

The package consists of 11 MATLAB scripts. Their functions are as follows:

1. class_AccPC.m: Main function of PCAcc method

2. analgroup.m: dividing samples into two groups

3. svdpca.m: applying PCA on the training set and projecting the test set

onto PC subspaces

4. disprotpcsallpoints.m: exhibiting samples distribution in PC plot

5. rotation.m: obtaining the resolutions of all the PC subspaces and finding

the PC subspace with maximal resolution

6. rotpcs: maximizing the distance between groups by using Fisher criterion

7. stats.m: selecting PC subspaces

8. dispsumR.m: exhibiting samples distribution along the projected or the

accumulated PC axis

9. dispsumRs.m: calculating mean value, standard deviation and resolution

10. disclass.m: assigning the class label of a sample

11. fastdist.m: calculating Euclidean distance between the samples with the

centroid of each group