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» Random Projections for Manifold Learning
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ECML
2004
Springer
14 years 4 months ago
The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering
This work presents a novel procedure for computing (1) distances between nodes of a weighted, undirected, graph, called the Euclidean Commute Time Distance (ECTD), and (2) a subspa...
Marco Saerens, François Fouss, Luh Yen, Pie...
PROMISE
2010
13 years 5 months ago
On the value of learning from defect dense components for software defect prediction
BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusi...
Hongyu Zhang, Adam Nelson, Tim Menzies
ICASSP
2011
IEEE
13 years 2 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
AAAI
2006
14 years 8 days ago
Tensor Embedding Methods
Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various machine learning and pattern classification tasks...
Guang Dai, Dit-Yan Yeung
NN
2007
Springer
13 years 10 months ago
Perception through visuomotor anticipation in a mobile robot
Several scientists suggested that certain perceptual qualities are based on sensorimotor anticipation: for example, the softness of a sponge is perceived by anticipating the sensa...
Heiko Hoffmann