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CVPR
2008
IEEE
14 years 11 months ago
Large-scale manifold learning
This paper examines the problem of extracting lowdimensional manifold structure given millions of highdimensional face images. Specifically, we address the computational challenge...
Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley
IJON
2010
152views more  IJON 2010»
13 years 8 months ago
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensiona...
Kerstin Bunte, Barbara Hammer, Axel Wismüller...
PR
2008
129views more  PR 2008»
13 years 9 months ago
A comparison of generalized linear discriminant analysis algorithms
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...
Cheong Hee Park, Haesun Park
BMVC
2010
13 years 7 months ago
Iterative Hyperplane Merging: A Framework for Manifold Learning
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Harry Strange, Reyer Zwiggelaar
MICCAI
2007
Springer
14 years 10 months ago
High Throughput Analysis of Breast Cancer Specimens on the Grid
Breast cancer accounts for about 30% of all cancers and 15% of all cancer deaths in women in the United States. Advances in computer assisted diagnosis (CAD) holds promise for earl...
Lin Yang, Wenjin Chen, Peter Meer, Gratian Salaru,...