Dimensionality reduction via graph structure learning

Qi Mao, Li Wang, Steve Goodison, Yijun Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations


We present a new dimensionality reduction setting for a large family of real-world problems. Unlike traditional methods, the new setting aims to explicitly represent and learn an intrinsic structure from data in a high-dimensional space, which can greatly facilitate data visualization and scientific discovery in downstream analysis. We propose a new dimensionality-reduction framework that involves the learning of a mapping function that projects data points in the original high-dimensional space to latent points in a low-dimensional space that are then used directly to construct a graph. Local geometric information of the projected data is naturally captured by the constructed graph. As a showcase, we develop a new method to obtain a discriminative and compact feature representation for clustering problems. In contrast to assumptions used in traditional clustering methods, we assume that centers of clusters should be close to each other if they are connected in a learned graph, and other cluster centers should be distant. Extensive experiments are performed that demonstrate that the proposed method is able to obtain discriminative feature representations yielding superior clustering performance, and correctly recover the intrinsic structures of various real-world datasets including curves, hierarchies and a cancer progression path.

Original languageEnglish (US)
Title of host publicationKDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450336642
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining


Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015


  • Clustering
  • Dimensionality reduction
  • Graph structure learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Software
  • Information Systems


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