Semi-supervised multi-view clustering by label relaxation based non-negative matrix factorization
- 31 March 2022
- journal article
- research article
- Published by Springer Nature in The Visual Computer
- Vol. 39 (4) , 1-14
Abstract
No abstract availableKeywords
Funding Information
- Key-Area Research and Development Program of Guangdong Province (2019B010154002, 2019B010118001, 2019B010121001)
- National Natural Science Foundation of China (61803096, 61801133, U191140003)
- Guangzhou Science and Technology Program Project (202002030289)
This publication has 50 references indexed in Scilit:
- Measuring mixing patterns in complex networks by Spearman rank correlation coefficientPhysica A: Statistical Mechanics and its Applications, 2016
- Label propagation based semi-supervised non-negative matrix factorization for feature extractionNeurocomputing, 2015
- Partially Shared Latent Factor Learning With Multiview DataIEEE Transactions on Neural Networks and Learning Systems, 2014
- Semi-supervised Unified Latent Factor learning with multi-view dataMachine Vision and Applications, 2013
- Multi-View Clustering via Joint Nonnegative Matrix Factorization2013
- A hierarchical clustering algorithm based on the Hungarian methodPattern Recognition Letters, 2008
- Orthogonal nonnegative matrix t-factorizations for clustering2006
- Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data valuesEnvironmetrics, 1994
- Comparing partitionsJournal of Classification, 1985
- The Hungarian method for the assignment problemNaval Research Logistics Quarterly, 1955