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Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming

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Figure

Figure 1: Motivation of NMF with sparseness constraints. Five basis functions (columns) withsparseness constraints ranging from 0.1 (first row, left) to 0.8 (last row, right) on W weretrained on the CBCL face database
Table 1: Unconstrained NMF. Comparison between QP algorithm and multiplicative updates(MU)
Figure 2: Paatero experiments. The entries of the factors W and H are displayed (Figure 2(a)) aswell as the resulting data matrix X (Figure 2(b))
Table 2: Sparsity-controlled NMF. Statistics of the Paatero experiment collected over 10 runs forthe tangent-plane constraints (TPC) and the sparsity-maximization algorithm (SMA)
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