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IEEEMM
2007
146views more  IEEEMM 2007»
13 years 3 months ago
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...
Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. N...
CPHYSICS
2007
93views more  CPHYSICS 2007»
13 years 3 months ago
Convergence rate of dimension reduction in Bose-Einstein condensates
In this paper, we study dimension reduction of the three-dimensional (3D) Gross–Pitaevskii equation (GPE) modeling Bose–Einstein condensation under different limiting interact...
Weizhu Bao, Yunyi Ge, Dieter Jaksch, Peter A. Mark...
BMCBI
2008
157views more  BMCBI 2008»
13 years 3 months ago
Dimension reduction with redundant gene elimination for tumor classification
Background: Analysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DN...
Xue-Qiang Zeng, Guo-Zheng Li, Jack Y. Yang, Mary Q...
BMCBI
2010
243views more  BMCBI 2010»
13 years 3 months ago
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data
Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new ...
Christoph Bartenhagen, Hans-Ulrich Klein, Christia...
ICANN
2010
Springer
13 years 3 months ago
Deep Bottleneck Classifiers in Supervised Dimension Reduction
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
Elina Parviainen
ICML
2010
IEEE
13 years 4 months ago
Projection Penalties: Dimension Reduction without Loss
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
Yi Zhang 0010, Jeff Schneider
ICMLA
2008
13 years 5 months ago
Graph-Based Multilevel Dimensionality Reduction with Applications to Eigenfaces and Latent Semantic Indexing
Dimension reduction techniques have been successfully applied to face recognition and text information retrieval. The process can be time-consuming when the data set is large. Thi...
Sophia Sakellaridi, Haw-ren Fang, Yousef Saad
GLOBECOM
2009
IEEE
13 years 7 months ago
Near-ML Detection over a Reduced Dimension Hypersphere
Abstract--In this paper, we propose a near-maximum likelihood (ML) detection method referred to as reduced dimension ML search (RD-MLS). The RD-MLS detector is based on a partition...
Jun Won Choi, Byonghyo Shim, Andrew C. Singer
CDC
2009
IEEE
185views Control Systems» more  CDC 2009»
13 years 8 months ago
Discrete Empirical Interpolation for nonlinear model reduction
A dimension reduction method called Discrete Empirical Interpolation (DEIM) is proposed and shown to dramatically reduce the computational complexity of the popular Proper Orthogo...
Saifon Chaturantabut, Danny C. Sorensen
ICDM
2003
IEEE
178views Data Mining» more  ICDM 2003»
13 years 9 months ago
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dime...
Qi Li, Jieping Ye, Chandra Kambhamettu