An online algorithm for efficient and temporally consistent subspace clustering

Vasileios Zografos, Kai Krajsek, Bjoern Menze

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present an online algorithm for the efficient clustering of data drawn from a union of arbitrary dimensional, non-static subspaces. Our algorithm is based on an online min-Mahalanobis distance classifier, which simultaneously clusters and is updated from subspace data. In contrast to most existing methods, our algorithm can cope with large amounts of batch or sequential data and is temporally consistent when dealing with time varying data (i.e. time-series). Starting from an initial condition, the classifier provides a first estimate of the subspace clusters in the current time-window. From this estimate, we update the classifier using stochastic gradient descent. The updated classifier is applied back onto the data to refine the subspace clusters, while at the same time we recover the explicit rotations that align the subspaces between time-windows. The whole procedure is repeated until convergence, resulting in a fast, efficient and accurate algorithm. We have tested our algorithm on synthetic and three real datasets and compared with competing methods from literature. Our results show that our algorithm outperforms the competition with superior clustering accuracy and computation speed.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
EditorsYoichi Sato, Ko Nishino, Vincent Lepetit, Shang-Hong Lai
PublisherSpringer Verlag
Pages353-368
Number of pages16
ISBN (Print)9783319541808
DOIs
StatePublished - 2017
Event13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China
Duration: 20 Nov 201624 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Asian Conference on Computer Vision, ACCV 2016
Country/TerritoryTaiwan, Province of China
City Taipei
Period20/11/1624/11/16

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