Visualizing Coclustered Matrices

Published:

Worked with Professor John Kender at Columbia University to provide visualization of the correlation between visual and textual memes in online video data and provided an analysis on the most effective ways of visualizing co-clustered data.

Abstract: Many videos on the Web about international events are maintained in different countries, and some come with text descriptions from different cultural points of view. We perform a spectral decomposition algorithm to cluster these videos based on their visual memes and their written tag identifiers. The spectral decomposition provides a matrix containing tags clustered with tags, and coclustered with visual memes, as well as visual memes clustered with visual memes and coclustered with tags. We take one of these coclustered matrices and provide a Web service for visualizing the clustering in scatterplot format, force-directed graph layout, and histograms. In addition we have demonstrated that Applying algorithms such as Reverse Cuthill McKee can allow for the viewer to see a diagonalized representation of the matrix.

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