Multi-view fusion with extreme learning machine for clustering

Yongshan Zhang, Jia Wu, Chuan Zhou, Zhihua Cai, Jian Yang, Philip S. Yu

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC's clustering accuracy.

Original languageEnglish
Article number53
Pages (from-to)1-23
Number of pages23
JournalACM Transactions on Intelligent Systems and Technology
Issue number5
Publication statusPublished - Oct 2019


  • Extreme learning machine
  • Multi-view clustering
  • Multi-view embedding
  • Unsupervised learning


Dive into the research topics of 'Multi-view fusion with extreme learning machine for clustering'. Together they form a unique fingerprint.

Cite this