Accelerating Infinite Ensemble of Clustering by Pivot Features |
| |
Authors: | Xiao-Bo Jin Guo-Sen Xie Kaizhu Huang Amir Hussain |
| |
Affiliation: | 1.College of Information Science and Engineering,Henan University of Technology,Zhengzhou,China;2.Inception Institute of Artificial Intelligence (IIAI),Abu Dhabi,UAE;3.College of Information Science and Engineering,Henan University of Science and Technology,Luoyang,China;4.Department of Electrical & Electronic Engineering,Xi’an Jiaotong-Liverpool University,Suzhou,China;5.Division of Computing Science & Maths, School of Natural Sciences,University of Stirling,Stirling,UK |
| |
Abstract: | 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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|