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README.md

Hierarchical Multiple Kernel Clustering (HMKC)

Matalb implementation for AAAI21 paper:

Introduction

Abstract

Current multiple kernel clustering algorithms compute a partition with the consensus kernel or graph learned from the pre-specified ones, while the emerging late fusion methods firstly construct multiple partitions from each kernel separately, and then obtain a consensus one with them. However, both of them directly distill the clustering information from kernels or graphs with size $\mathbb{R}^{n\times n}$ to partition matrices with size $\mathbb{R}^{n\times k}$ , where n and k are the number of samples and clusters, respectively. This sudden drop of dimension would result in the loss of advantageous details for clustering. In this paper, we provide a brief insight of the aforementioned issue and propose a hierarchical approach to perform clustering while preserving advantageous details maximumly. Specifically, we gradually group samples into ${c_t}_{t=1}^s$ clusters, together with generating a sequence of intermediary matrices with size $\mathcal{R}^{n\times c_t}$, in which $n>c_1>\cdots>c_s>k$. A consensus partition with size $\mathbb{R}^{n\times k}$ is simultaneously learned and conversely guides the construction of intermediary matrices. This cyclic process is modeled into an unified objective and an alternative algorithm is designed to solve it. In addition, the proposed method is validated and compared with other representative multiple kernel clustering algorithms on benchmark datasets, demonstrating state-of-the-art performance by a large margin.

Structure

structure

Citation

If you find our code useful, please cite:

@inproceedings{DBLP:conf/aaai/JiyuanAAAI21Hierarchical,
  author    = {Jiyuan Liu and
               Xinwang Liu and
               Yuexiang Yang and
               Siwei Wang and
               Sihang Zhou},
  title     = {Hierarchical Multiple Kernel Clustering},
  booktitle = {Proceedings of the Thirty-fifth {AAAI} Conference on Artificial 	Intelligence (AAAI-21), Virtually, February 2-9, 2021},
  pages     = {},
  year      = {2021},
  crossref  = {},
  url       = {},
}

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简介

Hierarchical Multiple Kernel Clustering

MATLAB M

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