Learning Feature Aware Metric

Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang
Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:286-301, 2016.

Abstract

Distance Metric Learning (DML) aims to find a distance metric, revealing feature relationship and satisfying restrictions between instances, for distance based classifiers, e.g., kNN. Most DML methods take all features into consideration while leaving the feature importance identification untouched. Feature selection methods, on the other hand, only focus on feature weights and are seldom directly designed for distance based classifiers. In this paper, we propose a Feature AwaRe Metric learning (FARM) method which not only learns the appropriate metric for distance constraints but also discovers significant features and their relationships. In FARM approach, we treat a distance metric as a combination of feature weighting and feature relationship discovering factors. Therefore, by decoupling the metric into two parts, it facilitates flexible regularizations for feature importance selection as well as feature relationship constructing. Simulations on artificial datasets clearly reveal the comprehensiveness of feature weighting for FARM. Experiments on real datasets validate the improvement of classification performance and the efficiency of our FARM approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v63-ye4, title = {Learning Feature Aware Metric}, author = {Ye, Han-Jia and Zhan, De-Chuan and Si, Xue-Min and Jiang, Yuan}, booktitle = {Proceedings of The 8th Asian Conference on Machine Learning}, pages = {286--301}, year = {2016}, editor = {Durrant, Robert J. and Kim, Kee-Eung}, volume = {63}, series = {Proceedings of Machine Learning Research}, address = {The University of Waikato, Hamilton, New Zealand}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v63/ye4.pdf}, url = {https://proceedings.mlr.press/v63/ye4.html}, abstract = {Distance Metric Learning (DML) aims to find a distance metric, revealing feature relationship and satisfying restrictions between instances, for distance based classifiers, e.g., kNN. Most DML methods take all features into consideration while leaving the feature importance identification untouched. Feature selection methods, on the other hand, only focus on feature weights and are seldom directly designed for distance based classifiers. In this paper, we propose a Feature AwaRe Metric learning (FARM) method which not only learns the appropriate metric for distance constraints but also discovers significant features and their relationships. In FARM approach, we treat a distance metric as a combination of feature weighting and feature relationship discovering factors. Therefore, by decoupling the metric into two parts, it facilitates flexible regularizations for feature importance selection as well as feature relationship constructing. Simulations on artificial datasets clearly reveal the comprehensiveness of feature weighting for FARM. Experiments on real datasets validate the improvement of classification performance and the efficiency of our FARM approach.} }
Endnote
%0 Conference Paper %T Learning Feature Aware Metric %A Han-Jia Ye %A De-Chuan Zhan %A Xue-Min Si %A Yuan Jiang %B Proceedings of The 8th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Robert J. Durrant %E Kee-Eung Kim %F pmlr-v63-ye4 %I PMLR %P 286--301 %U https://proceedings.mlr.press/v63/ye4.html %V 63 %X Distance Metric Learning (DML) aims to find a distance metric, revealing feature relationship and satisfying restrictions between instances, for distance based classifiers, e.g., kNN. Most DML methods take all features into consideration while leaving the feature importance identification untouched. Feature selection methods, on the other hand, only focus on feature weights and are seldom directly designed for distance based classifiers. In this paper, we propose a Feature AwaRe Metric learning (FARM) method which not only learns the appropriate metric for distance constraints but also discovers significant features and their relationships. In FARM approach, we treat a distance metric as a combination of feature weighting and feature relationship discovering factors. Therefore, by decoupling the metric into two parts, it facilitates flexible regularizations for feature importance selection as well as feature relationship constructing. Simulations on artificial datasets clearly reveal the comprehensiveness of feature weighting for FARM. Experiments on real datasets validate the improvement of classification performance and the efficiency of our FARM approach.
RIS
TY - CPAPER TI - Learning Feature Aware Metric AU - Han-Jia Ye AU - De-Chuan Zhan AU - Xue-Min Si AU - Yuan Jiang BT - Proceedings of The 8th Asian Conference on Machine Learning DA - 2016/11/20 ED - Robert J. Durrant ED - Kee-Eung Kim ID - pmlr-v63-ye4 PB - PMLR DP - Proceedings of Machine Learning Research VL - 63 SP - 286 EP - 301 L1 - http://proceedings.mlr.press/v63/ye4.pdf UR - https://proceedings.mlr.press/v63/ye4.html AB - Distance Metric Learning (DML) aims to find a distance metric, revealing feature relationship and satisfying restrictions between instances, for distance based classifiers, e.g., kNN. Most DML methods take all features into consideration while leaving the feature importance identification untouched. Feature selection methods, on the other hand, only focus on feature weights and are seldom directly designed for distance based classifiers. In this paper, we propose a Feature AwaRe Metric learning (FARM) method which not only learns the appropriate metric for distance constraints but also discovers significant features and their relationships. In FARM approach, we treat a distance metric as a combination of feature weighting and feature relationship discovering factors. Therefore, by decoupling the metric into two parts, it facilitates flexible regularizations for feature importance selection as well as feature relationship constructing. Simulations on artificial datasets clearly reveal the comprehensiveness of feature weighting for FARM. Experiments on real datasets validate the improvement of classification performance and the efficiency of our FARM approach. ER -
APA
Ye, H., Zhan, D., Si, X. & Jiang, Y.. (2016). Learning Feature Aware Metric. Proceedings of The 8th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 63:286-301 Available from https://proceedings.mlr.press/v63/ye4.html.

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