TY - GEN
T1 - An online algorithm for efficient and temporally consistent subspace clustering
AU - Zografos, Vasileios
AU - Krajsek, Kai
AU - Menze, Bjoern
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015981495&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54181-5_23
DO - 10.1007/978-3-319-54181-5_23
M3 - Conference contribution
AN - SCOPUS:85015981495
SN - 9783319541808
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 353
EP - 368
BT - Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Sato, Yoichi
A2 - Nishino, Ko
A2 - Lepetit, Vincent
A2 - Lai, Shang-Hong
PB - Springer Verlag
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
Y2 - 20 November 2016 through 24 November 2016
ER -