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ICIP
2010
IEEE
14 years 7 months ago
Exemplar-Based EM-like image denoising via manifold reconstruction
Discovering local geometry of low-dimensional manifold embedded into a high-dimensional space has been widely studied in the literature of machine learning. Counter-intuitively, w...
Xin Li
SIGGRAPH
2010
ACM
15 years 1 months ago
Manifold bootstrapping for SVBRDF capture
Manifold bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance, based on the idea that reflectance over a given material sample forms...
Yue Dong, Jiaping Wang, Xin Tong, John Snyder, Yan...
PKDD
2005
Springer
131views Data Mining» more  PKDD 2005»
15 years 3 months ago
ISOLLE: Locally Linear Embedding with Geodesic Distance
Locally Linear Embedding (LLE) has recently been proposed as a method for dimensional reduction of high-dimensional nonlinear data sets. In LLE each data point is reconstructed fro...
Claudio Varini, Andreas Degenhard, Tim W. Nattkemp...
ERSA
2008
103views Hardware» more  ERSA 2008»
14 years 11 months ago
A Hardware Accelerator for k-th Nearest Neighbor Thinning
This paper presents an accelerator for k-th nearest neighbor thinning, a run time intensive algorithmic kernel used in recent multi-objective optimizers. We discuss the thinning al...
Tobias Schumacher, Robert Meiche, Paul Kaufmann, E...
SDM
2007
SIAM
126views Data Mining» more  SDM 2007»
14 years 11 months ago
Nonlinear Dimensionality Reduction using Approximate Nearest Neighbors
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...
Erion Plaku, Lydia E. Kavraki