Accelerating Infinite Ensemble of Clustering by Pivot Features

Xiao Bo Jin*, Guo Sen Xie, Kaizhu Huang, Amir Hussain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation system with the high time complexity in proportion to O(d3) where d is the concatenated dimension of many clustering results. Inspired by the cognitive characteristic of human memory that can pay attention to the pivot features in a more compressed data space, we propose an acceleration version of IEC (AIEC) by extracting the pivot features and learning the multiple mappings to reconstruct them, where the linear equation system can be solved with the time complexity O(dr2) (r ≪ d). Experimental results on the standard datasets including image and text ones show that our algorithm AIEC improves the running time of IEC greatly but achieves the comparable clustering performance.

Original languageEnglish
Pages (from-to)1042-1050
Number of pages9
JournalCognitive Computation
Issue number6
Publication statusPublished - 1 Dec 2018


  • Ensemble clustering
  • Infinite ensemble clustering
  • Pivot features
  • Reconstruction of features

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