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Systematic study on dimensionality reduction in the gesture phase segmentation problem

In this paper, we present the results obtained in a systematic study related to dimensionality reduction on gesture windowed data. The aim of the study was to analyze the effects that such reduction causes on the performance of classification models used to implement the gesture phase segmentation task. Piecewise Aggregate Approximation was used to implement the dimensionality reduction and k-Nearest Neighbors was used to implement the classification models. The results showed that the dimensionality reduction can improve the classification models’ performance both by decreasing the complexity of their decision space and by improving the quality of the data.