CCP

Correlated Clustering and Projection for Dimensionality Reduction

CCP is a data-domain dimensionality reduction algorithm, and has the following 2 steps.

1. The features are clustered, according to their correlation/similarity
2. The features clusters are projected using flexiblity rigidity index into 1 descriptor

Full details of the methodology and theoretical motivation can be found on https://arxiv.org/abs/2206.04189

A simple example is performed using the TCGA-PANCAN data, which can be downloaded from UCI repository https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq.

1. Download the data and extract the file into toy_data
2. run the command example.py

Please use the following citation: @article{hozumi2022ccp,

title={Ccp: Correlated clustering and projection for dimensionality reduction},

author={Hozumi, Yuta and Wang, Rui and Wei, Guo-Wei},

journal={arXiv preprint arXiv:2206.04189},

year={2022} }