Abstract
The paper discusses 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 cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.
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Bacciu, D. (2014). An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_4
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DOI: https://doi.org/10.1007/978-3-319-11071-4_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11070-7
Online ISBN: 978-3-319-11071-4
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