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» A Distance-Based Packing Method for High Dimensional Data
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BMCBI
2010
118views more  BMCBI 2010»
13 years 9 months ago
Predicting nucleosome positioning using a duration Hidden Markov Model
Background: The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasi...
Liqun Xi, Yvonne Fondufe-Mittendorf, Lei Xia, Jare...
NIPS
2007
13 years 10 months ago
Random Projections for Manifold Learning
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
Chinmay Hegde, Michael B. Wakin, Richard G. Barani...
CSDA
2007
114views more  CSDA 2007»
13 years 8 months ago
Relaxed Lasso
The Lasso is an attractive regularisation method for high dimensional regression. It combines variable selection with an efficient computational procedure. However, the rate of co...
Nicolai Meinshausen
ICANN
2001
Springer
14 years 1 months ago
Generalized Relevance LVQ for Time Series
Abstract. An application of the recently proposed generalized relevance learning vector quantization (GRLVQ) to the analysis and modeling of time series data is presented. We use G...
Marc Strickert, Thorsten Bojer, Barbara Hammer
ICIP
2007
IEEE
14 years 19 days ago
Unsupervised Nonlinear Manifold Learning
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...
Matthieu Brucher, Christian Heinrich, Fabrice Heit...