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GRC
2008
IEEE
15 years 7 months ago
Neighborhood Smoothing Embedding for Noisy Manifold Learning
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Guisheng Chen, Junsong Yin, Deyi Li
ICML
2010
IEEE
15 years 7 months ago
Efficient Learning with Partially Observed Attributes
We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the ca...
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, O...
CVPR
2009
IEEE
17 years 1 months ago
Learning Rotational Features for Filament Detection
State-of-the-art approaches for detecting filament-like structures in noisy images rely on filters optimized for signals of a particular shape, such as an ideal edge or ridge. W...
François Fleuret, Germán Gonzá...
ICCV
2009
IEEE
16 years 11 months ago
Constrained Clustering by Spectral Kernel Learning
Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify s...
Zhenguo Li, Jianzhuang Liu
CVPR
2003
IEEE
16 years 8 months ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
Robert Fergus, Pietro Perona, Andrew Zisserman