SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition
نویسندگان
چکیده
Singular value decomposition is used to extract features from timeseries motion data. A matrix consisting of the time-series data is decomposed into left singular vectors which represent the patterns of the motion and singular values as a scalar, by which each corresponding left singular vector affects the matrix. Gesture recognition using the extracted features suggest the effectiveness of the method.
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