Predictable Dual-View Hashing

Mohammad Rastegari, Jonghyun Choi, Shobeir Fakhraei, Daume Hal, Larry Davis
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1328-1336, 2013.

Abstract

We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-rastegari13, title = {Predictable Dual-View Hashing}, author = {Rastegari, Mohammad and Choi, Jonghyun and Fakhraei, Shobeir and Hal, Daume and Davis, Larry}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {1328--1336}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/rastegari13.pdf}, url = {https://proceedings.mlr.press/v28/rastegari13.html}, abstract = {We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.} }
Endnote
%0 Conference Paper %T Predictable Dual-View Hashing %A Mohammad Rastegari %A Jonghyun Choi %A Shobeir Fakhraei %A Daume Hal %A Larry Davis %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-rastegari13 %I PMLR %P 1328--1336 %U https://proceedings.mlr.press/v28/rastegari13.html %V 28 %N 3 %X We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.
RIS
TY - CPAPER TI - Predictable Dual-View Hashing AU - Mohammad Rastegari AU - Jonghyun Choi AU - Shobeir Fakhraei AU - Daume Hal AU - Larry Davis BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/26 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-rastegari13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 3 SP - 1328 EP - 1336 L1 - http://proceedings.mlr.press/v28/rastegari13.pdf UR - https://proceedings.mlr.press/v28/rastegari13.html AB - We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms. ER -
APA
Rastegari, M., Choi, J., Fakhraei, S., Hal, D. & Davis, L.. (2013). Predictable Dual-View Hashing. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(3):1328-1336 Available from https://proceedings.mlr.press/v28/rastegari13.html.

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