A sparse kernelized matrix learning vector quantization model for human activity recognition
نویسندگان
چکیده
The contribution describes our application to the ESANN'2013 Competition on Human Activity Recognition (HAR) using Android-OS smartphone sensor signals. We applied a kernel variant of learning vector quantization with metric adaptation using only one prototype vector per class. This sparse model obtains very good accuracies and additionally provides class correlation information. Further, the model allows an optimized class visualization.
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