- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Unions of subspaces have recently been shown to provide a compact nonlinear signal model for collections of high-dimensional data, such as large collections of images or videos. In this paper, we introduce a novel data-driven algorithm for learning unions of subspaces directly from a collection of data; our approach is based upon forming minimum l2 -norm (least-squares) representations of a signal with respect to other signals in the collection. The resulting representations are then used as feature vectors to cluster the data in accordance with each signal's subspace membership. We demonstrate that the proposed least-squares approach leads to improved classification performance when compared to state-of-the-art subspace clustering methods on both synthetic and real-world experiments. This study provides evidence that using least-squares methods to form data-driven representations of collections of data provide significant advantages over current methods that rely upon sparse representations. Show more
Book title2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Pages / Article No.
SubjectSubspace clustering; Unions of subspaces; Least-squares methods; Sparsity; Sparse recovery methods
Organisational unit09695 - Studer, Christoph / Studer, Christoph
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