TY - GEN
T1 - Non-linear metric learning using metric tensor
AU - Yin, Liangying
AU - Pei, Mingtao
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Manifold based metric learning methods have become increasingly popular in recent years. In almost all these methods, however, the underlying manifold is approximated by a point cloud, and the matric tensor, which is the most basic concept to describe the manifold, is neglected. In this paper, we propose a non-linear metric learning framework based on metric tensor. We construct a Riemannian manifold and its metric tensor on sample space, and replace the Euclidean metric by the learned Riemannian metric. By doing this, the sample space is twisted to a more suitable form for classification, clustering and other applications. The classification and clustering results on several public datasets show that the learned metric is effective and promising.
AB - Manifold based metric learning methods have become increasingly popular in recent years. In almost all these methods, however, the underlying manifold is approximated by a point cloud, and the matric tensor, which is the most basic concept to describe the manifold, is neglected. In this paper, we propose a non-linear metric learning framework based on metric tensor. We construct a Riemannian manifold and its metric tensor on sample space, and replace the Euclidean metric by the learned Riemannian metric. By doing this, the sample space is twisted to a more suitable form for classification, clustering and other applications. The classification and clustering results on several public datasets show that the learned metric is effective and promising.
KW - Gaussian mixture model
KW - Manifold
KW - Metric tensor
KW - Non-linear metric learning
UR - http://www.scopus.com/inward/record.url?scp=84952779884&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26532-2_4
DO - 10.1007/978-3-319-26532-2_4
M3 - Conference contribution
AN - SCOPUS:84952779884
SN - 9783319265315
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 37
BT - Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
A2 - Lai, Weng Kin
A2 - Liu, Qingshan
A2 - Huang, Tingwen
A2 - Arik, Sabri
PB - Springer Verlag
T2 - 22nd International Conference on Neural Information Processing, ICONIP 2015
Y2 - 9 November 2015 through 12 November 2015
ER -