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» Semi-Supervised Dimensionality Reduction
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126
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EUROCAST
2003
Springer
138views Hardware» more  EUROCAST 2003»
15 years 9 months ago
Coloring of DT-MRI Fiber Traces Using Laplacian Eigenmaps
We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian ei...
Anders Brun, Hae-Jeong Park, Hans Knutsson, Carl-F...
114
Voted
ICANN
2003
Springer
15 years 9 months ago
Supervised Locally Linear Embedding
Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti ...
146
Voted
ICPR
2002
IEEE
15 years 8 months ago
Determining a Suitable Metric when Using Non-Negative Matrix Factorization
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or partbased representation of o...
David Guillamet, Jordi Vitrià
125
Voted
NIPS
2004
15 years 5 months ago
Semi-parametric Exponential Family PCA
We present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the late...
Sajama, Alon Orlitsky
106
Voted
IJON
2006
85views more  IJON 2006»
15 years 3 months ago
From outliers to prototypes: Ordering data
We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show ...
Stefan Harmeling, Guido Dornhege, David M. J. Tax,...