NDLI: Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods

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Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods

Content Provider	ACM Digital Library
Author	Wang, Suhang Liu, Huan Aggarwal, Charu
Abstract	Feature engineering has found increasing interest in recent years because of its ability to improve the effectiveness of various machine learning models. Although tailored feature engineering methods have been designed for various domains, there are few that simulate the consistent effectiveness of kernel methods. At the core, the success of kernel methods is achieved by using similarity functions that emphasize local variations in similarity. Unfortunately, this ability comes at the price of the high level of computational resources required and the inflexibility of the representation as it only provides the similarity of two data points instead of vector representations of each data point; while the vector representations can be readily used as input to facilitate various models for different tasks. Furthermore, kernel methods are also highly susceptible to overfitting and noise and it cannot capture the variety of data locality. In this paper, we first analyze the inner working and weaknesses of kernel method, which serves as guidance for designing feature engineering. With the guidance, we explore the use of randomized methods for feature engineering by capturing multi-granular locality of data. This approach has the merit of being time and space efficient for feature construction. Furthermore, the approach is resistant to overfitting and noise because the randomized approach naturally enables fast and robust ensemble methods. Extensive experiments on a number of real world datasets are conducted to show the effectiveness of the approach for various tasks such as clustering, classification and outlier detection.
Starting Page	485
Ending Page	494
Page Count	10
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098001
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Unsupervised feature learning Randomized feature engineering
Content Type	Text
Resource Type	Article

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