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CVPR
2010
IEEE
14 years 5 months ago
Manifold Blurring Mean Shift Algorithms
We propose a new family of algorithms for denoising data assumed to lie on a low-dimensional manifold. The algorithms are based on the blurring mean-shift update, which moves each...
Weiran Wang, Miguel Carreira-perpinan
ICDM
2005
IEEE
165views Data Mining» more  ICDM 2005»
14 years 3 months ago
A Bernoulli Relational Model for Nonlinear Embedding
The notion of relations is extremely important in mathematics. In this paper, we use relations to describe the embedding problem and propose a novel stochastic relational model fo...
Gang Wang, Hui Zhang, Zhihua Zhang, Frederick H. L...
PKDD
2005
Springer
131views Data Mining» more  PKDD 2005»
14 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...
ICASSP
2010
IEEE
13 years 10 months ago
Evaluation of random-projection-based feature combination on speech recognition
Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computati...
Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yas...
SBBD
2004
94views Database» more  SBBD 2004»
13 years 11 months ago
Visual Analysis of Feature Selection for Data Mining Processes
The amount of data collected in the last decades has become a source of valuable information, allowing organizations to improve their competitiveness. However, the associated data...
Humberto Luiz Razente, Fabio Jun Takada Chino, Mar...