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A principled approach to image denoising with similarity kernels involving patches.

, , and . Neurocomputing, 73 (7-9): 1199-1209 (2010)

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On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures., , and . ESANN, page 99-106. (2003)Curvilinear Distance Analysis versus Isomap., , and . ESANN, page 185-192. (2002)Sensitivity to parameter and data variations in dimensionality reduction techniques., , , and . ESANN, (2013)Recent methods for dimensionality reduction: A brief comparative analysis., , and . ESANN, (2014)How to project `circular' manifolds using geodesic distances?, and . ESANN, page 223-230. (2004)Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding., , and . WSOM, volume 295 of Advances in Intelligent Systems and Computing, page 65-74. Springer, (2014)Generalized kernel framework for unsupervised spectral methods of dimensionality reduction., , and . CIDM, page 171-177. IEEE, (2014)Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation., , , , and . Neurocomputing, (2013)Incremental classification of objects in scenes: Application to the delineation of images., , and . Neurocomputing, (2015)Forecasting the CATS benchmark with the Double Vector Quantization method., , , and . Neurocomputing, 70 (13-15): 2400-2409 (2007)