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.
|Titolo:||An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications|
|Anno del prodotto:||2014|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|