Localized K-Flats


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


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.




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



AAAI Technical Track: Machine Learning