Localized K-Flats

Authors

  • Yong Wang National University of Defense Technology
  • Yuan Jiang Nanjing University
  • Yi Wu National University of Defense Technology
  • Zhi-Hua Zhou Nanjing University

Abstract

K-flats is a model-based linear manifold clustering algorithm which has been successfully applied in many real-world scenarios. Though some previous works have shown that K-flats doesn’t always provide good performance, little effort has been devoted to analyze its inherent deficiency. In this paper, we address this challenge by showing that the deteriorative performance of K-flats can be attributed to the usual reconstruction error measure and the infinitely extending representations of linear models. Then we propose Localized K-flats algorithm (LKF), which introduces localized representations of linear models and a new distortion measure, to remove confusion among different clusters. Experiments on both synthetic and real-world data sets demonstrate the efficiency of the proposed algorithm. Moreover, preliminary experiments show that LKF has the potential to group manifolds with nonlinear structure.

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Published

2011-08-04

How to Cite

Wang, Y., Jiang, Y., Wu, Y., & Zhou, Z.-H. (2011). Localized K-Flats. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 525-530. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7912

Issue

Section

AAAI Technical Track: Machine Learning