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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/s10600-009-9425-8

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1433-3058

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Unsupervised feature selection for sensor timeser

Authors: Davide Bacciu
Publish Date: 2015/05/22
Volume: 27, Issue: 5, Pages: 1077-1091
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Abstract

The paper introduces an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario An iterative filtering procedure is devised to reduce information redundancy measured in terms of timeseries crosscorrelation The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features In particular the proposed feature selection process does not require expert intervention to determine the number of selected features which is a key advancement with respect to timeseries filters in the literature The characteristic of the prosed algorithm allows enriching learning systems in pervasive computing applications with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation A comparative experimental analysis on realworld data from three pervasive computing applications is provided showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor timeseries Specifically it is presented an assessment both in terms of reduction of timeseries redundancy and in terms of preservation of informative features with respect to associated supervised learning tasksThis work is supported by the FP7 RUBICON Project Contract No 269914 The author would like to thank Claudio Gallicchio for providing part of the results on the Echo State Network experiment as well as Filippo Barontini for the collection of the HAR dataset

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