Soft Clustering with Projections: PCA, ICA, and Laplacian
نویسندگان
چکیده
In this paper we present a comparison of three projection methods that use the eigenvectors of a matrix to investigate high-dimensional dataset: principal component analysis (PCA), principal component analysis followed by independent component analysis (PCA+ICA), and Laplacian projections. We demonstrate the application of these methods to a sponsored links search listings dataset and provide a comparison of the results both by examining the qualities of the projected dataset and looking at the topics represented by each soft cluster.
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تاریخ انتشار 2005