— 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...
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...
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...
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...
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
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...
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...
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...
A dimension reduction method called Discrete Empirical Interpolation (DEIM) is proposed and shown to dramatically reduce the computational complexity of the popular Proper Orthogo...
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...