A Survey of Modern Questions and Challenges in Feature Extraction

Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar
Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, PMLR 44:1-18, 2015.

Abstract

The problem of extracting features from given data is of critical importance for successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them.

Cite this Paper

BibTeX
@InProceedings{pmlr-v44-storcheus2015survey, title = {A Survey of Modern Questions and Challenges in Feature Extraction}, author = {Storcheus, Dmitry and Rostamizadeh, Afshin and Kumar, Sanjiv}, booktitle = {Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015}, pages = {1--18}, year = {2015}, editor = {Storcheus, Dmitry and Rostamizadeh, Afshin and Kumar, Sanjiv}, volume = {44}, series = {Proceedings of Machine Learning Research}, address = {Montreal, Canada}, month = {11 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v44/storcheus2015survey.pdf}, url = {https://proceedings.mlr.press/v44/storcheus2015survey.html}, abstract = {The problem of extracting features from given data is of critical importance for successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them.} }
Endnote
%0 Conference Paper %T A Survey of Modern Questions and Challenges in Feature Extraction %A Dmitry Storcheus %A Afshin Rostamizadeh %A Sanjiv Kumar %B Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015 %C Proceedings of Machine Learning Research %D 2015 %E Dmitry Storcheus %E Afshin Rostamizadeh %E Sanjiv Kumar %F pmlr-v44-storcheus2015survey %I PMLR %P 1--18 %U https://proceedings.mlr.press/v44/storcheus2015survey.html %V 44 %X The problem of extracting features from given data is of critical importance for successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them.
RIS
TY - CPAPER TI - A Survey of Modern Questions and Challenges in Feature Extraction AU - Dmitry Storcheus AU - Afshin Rostamizadeh AU - Sanjiv Kumar BT - Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015 DA - 2015/12/08 ED - Dmitry Storcheus ED - Afshin Rostamizadeh ED - Sanjiv Kumar ID - pmlr-v44-storcheus2015survey PB - PMLR DP - Proceedings of Machine Learning Research VL - 44 SP - 1 EP - 18 L1 - http://proceedings.mlr.press/v44/storcheus2015survey.pdf UR - https://proceedings.mlr.press/v44/storcheus2015survey.html AB - The problem of extracting features from given data is of critical importance for successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them. ER -
APA
Storcheus, D., Rostamizadeh, A. & Kumar, S.. (2015). A Survey of Modern Questions and Challenges in Feature Extraction. Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, in Proceedings of Machine Learning Research 44:1-18 Available from https://proceedings.mlr.press/v44/storcheus2015survey.html.

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