A matlab implementation of paper "Collaborative feature-weighted multi-view fuzzy c-means clustering."

Published in Pattern Recognition '21

Link: https://www.sciencedirect.com/science/article/abs/pii/S003132032100251X

Main Function: MSRC_CoFWMVFCM

In case the repository or the publication was helpful in your work, please use the following to cite the original paper,

 @article{yang2021collaborative,

  title={Collaborative feature-weighted multi-view fuzzy c-means clustering},

  author={Yang, Miin-Shen and Sinaga, Kristina P},

  journal={Pattern Recognition},

  pages={108064},

  year={2021},

  publisher={Elsevier}

}

Fuzzy c-means (FCM) clustering had been extended for handling multi-view data with collaborative idea. However, these collaborative multi-view FCM treats multi-view data under equal importance of feature components. In general, different features should take different weights for clustering real multi-view data. In this paper, we propose a novel multi-view FCM (MVFCM) clustering algorithm with view and feature weights based on collaborative learning, called collaborative feature-weighted MVFCM (Co-FW-MVFCM). The Co-FW-MVFCM contains a two-step schema that includes a local step and a collaborative step. The local step is a single-view partition process to produce local partition clustering in each view, and the collaborative step is sharing information of their memberships between different views. These two steps are then continuing by an aggregation way to get a global result after collaboration. Furthermore, the embedded feature-weighted procedure in Co-FW-MVFCM can give feature reduction to exclude redundant/irrelevant feature components during clustering processes. Experiments with several data sets demonstrate that the proposed Co-FW-MVFCM algorithm can completely identify irrelevant feature components in each view and that, additionally, it can improve the performance of the algorithm. Comparisons of Co-FW-MVFCM with some existing MVFCM algorithms are made and also demonstrated the effectiveness and usefulness of the proposed Co-FW-MVFCM clustering algorithm.

The Microsoft Research Cambridge Volume 1 (MSRC-V1) data set from Microsoft Research in Cambridge are collected by Jamie Shotton, John Winn, Tom Minka, and Toby Sharp. This MSRC-V1 data set openly available and accessible at http://research.microsoft.com/en-us/projects/objectclassrecognition/ website. The data set has eight classes, and each class has 30 images. These eight classes are horses, trees, buildings, airplanes, cow, face, car, and bicycle. In our experiments, we select 210 images and seven classes. They are trees, buildings, airplanes, cow, face, car, and bicycle. Five different views represent these 210 images: 24 features of the color moment, 254 features of CENTRIST, 512 features of GIST, 576 features of the histogram of oriented gradients (HOG), and 256 features of local binary patterns (LBP).