Visualizing pairwise similarity via semidefinite programming

Amir Globerson, Sam Roweis
; Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:139-146, 2007.

Abstract

We introduce a novel learning algorithm for binary pairwise similarity measurements on a set of objects. The algorithm delivers an embedding of the objects into a vector representation space that strictly respects the known similarities, in the sense that objects known to be similar are always closer in the embedding than those known to be dissimilar. Subject to this constraint, our method selects the mapping in which the variance of the embedded points is maximized. This has the effect of favoring embeddings with low effective dimensionality. The related optimization problem can be cast as a convex Semidefinite Program (SDP). We also present a parametric version of the problem, which can be used for embedding out of sample points. The parametric version uses kernels to obtain nonlinear maps, and can also be solved using an SDP. We apply the two algorithms to an image embedding problem, where it effectively captures the low dimensional structure corresponding to camera viewing parameters.

Cite this Paper


BibTeX
@InProceedings{pmlr-v2-globerson07b, title = {Visualizing pairwise similarity via semidefinite programming}, author = {Amir Globerson and Sam Roweis}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {139--146}, year = {2007}, editor = {Marina Meila and Xiaotong Shen}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/globerson07b/globerson07b.pdf}, url = {http://proceedings.mlr.press/v2/globerson07b.html}, abstract = {We introduce a novel learning algorithm for binary pairwise similarity measurements on a set of objects. The algorithm delivers an embedding of the objects into a vector representation space that strictly respects the known similarities, in the sense that objects known to be similar are always closer in the embedding than those known to be dissimilar. Subject to this constraint, our method selects the mapping in which the variance of the embedded points is maximized. This has the effect of favoring embeddings with low effective dimensionality. The related optimization problem can be cast as a convex Semidefinite Program (SDP). We also present a parametric version of the problem, which can be used for embedding out of sample points. The parametric version uses kernels to obtain nonlinear maps, and can also be solved using an SDP. We apply the two algorithms to an image embedding problem, where it effectively captures the low dimensional structure corresponding to camera viewing parameters.} }
Endnote
%0 Conference Paper %T Visualizing pairwise similarity via semidefinite programming %A Amir Globerson %A Sam Roweis %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-globerson07b %I PMLR %J Proceedings of Machine Learning Research %P 139--146 %U http://proceedings.mlr.press %V 2 %W PMLR %X We introduce a novel learning algorithm for binary pairwise similarity measurements on a set of objects. The algorithm delivers an embedding of the objects into a vector representation space that strictly respects the known similarities, in the sense that objects known to be similar are always closer in the embedding than those known to be dissimilar. Subject to this constraint, our method selects the mapping in which the variance of the embedded points is maximized. This has the effect of favoring embeddings with low effective dimensionality. The related optimization problem can be cast as a convex Semidefinite Program (SDP). We also present a parametric version of the problem, which can be used for embedding out of sample points. The parametric version uses kernels to obtain nonlinear maps, and can also be solved using an SDP. We apply the two algorithms to an image embedding problem, where it effectively captures the low dimensional structure corresponding to camera viewing parameters.
RIS
TY - CPAPER TI - Visualizing pairwise similarity via semidefinite programming AU - Amir Globerson AU - Sam Roweis BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics PY - 2007/03/11 DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-globerson07b PB - PMLR SP - 139 DP - PMLR EP - 146 L1 - http://proceedings.mlr.press/v2/globerson07b/globerson07b.pdf UR - http://proceedings.mlr.press/v2/globerson07b.html AB - We introduce a novel learning algorithm for binary pairwise similarity measurements on a set of objects. The algorithm delivers an embedding of the objects into a vector representation space that strictly respects the known similarities, in the sense that objects known to be similar are always closer in the embedding than those known to be dissimilar. Subject to this constraint, our method selects the mapping in which the variance of the embedded points is maximized. This has the effect of favoring embeddings with low effective dimensionality. The related optimization problem can be cast as a convex Semidefinite Program (SDP). We also present a parametric version of the problem, which can be used for embedding out of sample points. The parametric version uses kernels to obtain nonlinear maps, and can also be solved using an SDP. We apply the two algorithms to an image embedding problem, where it effectively captures the low dimensional structure corresponding to camera viewing parameters. ER -
APA
Globerson, A. & Roweis, S.. (2007). Visualizing pairwise similarity via semidefinite programming. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in PMLR 2:139-146

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