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 di?erent subspaces reveal difierent 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 ?nds 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.