Abstract
Sparsity Preserving Projections (SPP) is a well known approach for feature extraction and dimensionality reduction. Its success is mainly attributed to its high quality graph which is constructed by sparse representation. As an instance of graph embedding, SPP can be formulated as regression model. Thus we apply the idea of collaborative graph embedding, which reformulates SPP as a collaborative representation model via imposing a L2-norm constraint to projections from the perspective of linear regression, to further enhance SPP. We call this novel SPP method Collaborative Sparsity Preserving Projections (CSPP). Experiment results on four popular face datasets, namely Yale, ORL, FERET and AR, show the effectiveness in feature extraction and the improvement of CSPP over SPP.
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