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Cited by 4 publications
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“…A. Clustering 1) Experimental Setup: Datasets: We perform our clustering experiments on 5 benchmark databases (as in [35], [36]): CMU PIE, ORL, YALE, MNIST and USPS. For the USPS and ORL datasets, we further run two types of experiments 1) on subset of datasets and 2) on full datasets.…”
Section: Resultsmentioning
confidence: 99%
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“…A. Clustering 1) Experimental Setup: Datasets: We perform our clustering experiments on 5 benchmark databases (as in [35], [36]): CMU PIE, ORL, YALE, MNIST and USPS. For the USPS and ORL datasets, we further run two types of experiments 1) on subset of datasets and 2) on full datasets.…”
Section: Resultsmentioning
confidence: 99%
“…Comparison with other methods: We compare the clustering performance of CPCA with 11 other models including: 1) k-means on original data 2) Laplacian Eigenmaps (LE) [4] 3) Locally Linear Embedding (LLE) [32] 4) Standard PCA 5) Graph Laplacian PCA (GLPCA) [14] 6) Manifold Regularized Matrix Factorization (MMF) [46] 7) Non-negative Matrix Factorization (NMF) [17] 8) Graph Regularized Nonnegative Matrix Factorization (GNMF) [6] 9) Robust PCA (RPCA) [7] 10) Robust PCA on Graphs (RPCAG) [35] and Table I: Summary of CPCA and its computational complexity for a dataset Y ∈ p×n . Throughout we assume that K, k, ρr, ρc, p n.…”
Section: Resultsmentioning
confidence: 99%
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Fast Robust PCA on Graphs

Shahid,
Perraudin,
Kalofolias
et al. 2015
Preprint
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