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Fig. 1 | Human-centric Computing and Information Sciences

Fig. 1

From: Improving clustering performance using independent component analysis and unsupervised feature learning

Fig. 1

Pipeline for processing. Each of the components contains the options available for implementation. The simplest processing pipeline to obtain clustering results consists of a L2-normalization on the data, followed by K-means clustering. The processing stream with the most components would consist: (1) L2-normalization followed by UFL using either RICA or SFT; (2) similarity graph construction; (3) GNMF or spectral decomposition followed by ICA blind source separation; and (4) K-means clustering

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