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Sharpened dimensionality reduction (SDR), which belongs to the class ofmultidimensional projection techniques, has recently been introduced to tacklethe challenges in the exploratory and visual analysis of high-dimensional data.SDR has been applied to various real-world datasets, such as human activitysensory data and astronomical datasets. However, manually labeling the samplesfrom the generated projection are expensive. To address this problem, wepropose here to use clustering methods such as k-means, HierarchicalClustering, Density-Based Spatial Clustering of Applications with Noise(DBSCAN), and Spectral Clustering to easily label the 2D projections ofhigh-dimensional data. We test our pipeline of SDR and the clustering methodson a range of synthetic and real-world datasets, including two different publichuman activity datasets extracted from smartphone accelerometer or gyroscoperecordings of various movements. We apply clustering to assess the visualcluster separation of SDR, both qualitatively and quantitatively. We concludethat clustering SDR results yields better labeling results than clusteringplain DR, and that k-means is the recommended clustering method for SDR interms of clustering accuracy, ease-of-use, and computational scalability.

Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering