This repository is our implementation of
The core idea is to learn weights of different views based on which view fits the prior distribution better. The framework starts from a Bayesian aspect and we show the connection between the proposed maximum joint probability and some existing clustering methods. In the following illustration, although the 2-th view is well-structured for clustering, it is not a preferable view as it does not fit the prior distribution, i.e., Gaussian distribution.
If you have any questions, pleas email:
hyzhang98@gmail.com or hyzhang98@mail.nwpu.edu.cn.
Please see the comments in the source code file.
@ARTICLE{MJP,
author={R. {Zhang} and H. {Zhang} and X. {Li}},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Maximum Joint Probability With Multiple Representations for Clustering},
year={2021},
volume={},
number={},
pages={1-11},
doi={10.1109/TNNLS.2021.3056420}
}