Sparse Manifold Clustering and Embedding

By Ehsan Elhamifar, René Vidal. Neural Information Processing Systems, 2011

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Piecewise Flat Embedding for Image Segmentation (2018, background): “SMCE constructs neighborhood relationships via selecting neighbors from the same manifold for each data point, which is solved as a sparse optimization problem.”
Warped Mixtures for Nonparametric Cluster Shapes (2012, unknown): “There exist non-probabilistic clustering methods which can ﬁnd clusters with complex shapes, such as spectral clustering [15] and nonlinear manifold clustering [16, 17].”
Automatic model construction with Gaussian processes (2014, unknown): “, 2002) and nonlinear manifold clustering (Cao and Haralick, 2006; Elhamifar and Vidal, 2011).”
Efficient Label Propagation (2014, methodology): “Moreover, our approach can handle several types of graphs such as the linear neighborhood graph and the sparse L 1 graph (Wang & Zhang, 2008; Elhamifar & Vidal, 2011).”
See all by looking at a few: Sparse modeling for finding representative objects (2012, methodology): “the NN method in the Extended YaleB dataset, the large gap between using all the training data and using the representatives obtained by different algorithms is mainly due the fact that the data in different classes are close to each other [13, 15], hence using a subset of the the training data can ”
Manifold-based Similarity Adaptation for Label Propagation (2013, background): “Regularized LLE [5, 8, 13, 14] can be interpreted as one realization of such an approach.”