Název:	Using mutual independence of slow features for improved information extraction and better hand-pose classification
Autoři:	Tewari, Aditya Taetz, Bertram Grandidier, Frédéric
Citace zdrojového dokumentu:	Journal of WSCG. 2015, vol. 23, no. 1, p. 35-43.
Datum vydání:	2015
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	článek article
URI:	http://wscg.zcu.cz/WSCG2015/!_2015_Journal_WSCG-No-1.pdf http://hdl.handle.net/11025/17142
ISSN:	1213–6972 (hardcopy) 1213–6980 (CD-ROM) 1213–6964 (online)
Klíčová slova:	SFA;identifikace držení ruky;extrakce znalostí;učení vlastností
Klíčová slova v dalším jazyce:	SFA;hand-pose identification;knowledge extraction;feature learning
Abstrakt v dalším jazyce:	We propose a Slow Feature Analysis (SFA) based classification of hand-poses and demonstrate that the property of mutual independence of the slow feature functions improves the classification performance. SFA extracts functions that describe trends in a time series data and is capable of isolating noise from information while conserving high-frequency components of the data which are consistently present over time or in the set of data points. SFA is a useful knowledge extraction method that can be modified to identify functions which are well suited for distinguishing classes. We show that by using the orthogonality property of SFA our information about classes can be increased. This is demonstrated by classification results on the well known MNIST dataset for hand written digit detection. Furthermore, we use a hand-pose dataset with five possible classes to show the performance of SFA. It consistently achieves a detection rate of over 96% for each class. We compare the classification results on shape descriptive physical features, on the Principal Component Analysis (PCA) and the non-linear dimensionality reduction (NLDR) for manifold learning. We show that a simple variance based decision algorithm for SFA gives higher recognition rates than K-Nearest Neighbour (KNN), on physical features, PCA and non-linear low dimensional representation. Finally, we examine Convolutional Neural Networks (CNN) in relation with SFA.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	Volume 23, Number 1 (2015)

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