Multiple Non-Redundant Spectral Clustering Views

Abstract

Many clustering algorithms only find one clustering solution. However, data can often be grouped and interpreted in many different ways. This is particularly true in the high-dimensional setting where different subspaces reveal different possible groupings of the data. Instead of committing to one clustering solution, here we introduce a novel method that can provide several non-redundant clustering solutions to the user. Our approach simultaneously learns non-redundant subspaces that provide multiple views and finds a clustering solution in each view. We achieve this by augmenting a spectral clustering objective function to incorporate dimensionality reduction and multiple views and to penalize for redundancy between the views.