/MVASM

The code of "Multi-view K-Means Clustering with Adaptive Sparse Memberships and Weight Allocation". TKDE 2020

Primary LanguageMATLABBSD 2-Clause "Simplified" LicenseBSD-2-Clause

This repository contains the PyTorch implementation for MVASM. TKDE 2020

Datasets

Three heterogeneous feature descriptors are ISO.m, LDA.m, and NPE.m.

Parameters

For different datasets, their parameters can be set as follows:

  • COIL: qStart = 1; qNum = 96; qStride = 0.01; rStart = 0; rNum = 4; rStride = 0.1
  • MNIST: qStart = 1.6; qNum = 2; qStride = 0.01; rStart = 0; rNum = 2; rStride = 0.1
  • YALE: qStart = 1; qNum = 11; qStride = 0.01; rStart = 0; rNum = 9; rStride = 0.1

Reference

  • Multi-View K-Means Clustering on Big Data. (IJCAI,2013).
  • Discriminatively Embedded K-Means for Multi-view Clustering. (CVPR,2016)
  • Robust and Sparse Fuzzy K-Means Clustering. (IJCAI2016)
  • A new simplex sparse learning model to measure data similarity for clustering (AAAI2015)
  • COMPACT: A Comparative Package for Clustering Assessment. (ISPA2005)
  • https://github.com/ZJULearning/MatlabFunc/tree/master/Clustering