Results 21 to 30 of about 375,641 (168)

Neighbors-Based Graph Construction for Dimensionality Reduction

IEEE Access, 2019
Dimensionality reduction is a fundamental task in the field of data mining and machine learning. In many scenes, examples in high-dimensional space usually lie on low-dimensional manifolds; thus, learning the low-dimensional embedding is important.
Hui Tian, Long Lan, Xiang Zhang, Zhigang Luo   +3 more
doaj   +1 more source

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction [PDF]

, 2010
It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square ...
A D’aspremont, B Efron, C Fyfe, CHQ Ding, CM Bishop, D Cai, D Tao, D Tao, D Tao, Dacheng Tao, DB Graham, DL Donoho, E Candes, EJ Candes, GH Golub, GM James, H Hotelling, H Zou, H Zou, H Zou, H Zou, HP Kriegel, J Fan, J Fan, J Lv, JB Tenenbaum, M Belkin, M Belkin, PJ Phillips, PN Belhumeur, R Tibshirani, RA Fisher, S Yan, ST Roweis, T Li, T Zhang, Tianyi Zhou, X He, X Li, Xindong Wu, Z Zhang   +40 more
core   +1 more source

Binary Whale Optimization Algorithm for Dimensionality Reduction

Mathematics, 2020
Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good ...
Abdelazim G. Hussien, Diego Oliva, Essam H. Houssein, Angel A. Juan, Xu Yu   +4 more
doaj   +1 more source

Cortical spatio-temporal dimensionality reduction for visual grouping [PDF]

, 2014
The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing.
Barbieri, Davide, Citti, Giovanna, Cocci, Giacomo, Sarti, Alessandro   +3 more
core   +2 more sources

Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

Genome Biology, 2019
Background Dimensionality reduction is an indispensable analytic component for many areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality reduction can allow for effective noise removal and facilitate many downstream ...
Shiquan Sun, Jiaqiang Zhu, Ying Ma, Xiang Zhou   +3 more
doaj   +1 more source

Dimensionality reduction method for hyperspectral image analysis based on rough set theory

European Journal of Remote Sensing, 2020
High-dimensional features often cause computational complexity and dimensionality curse. Feature selection and feature extraction are the two mainstream methods for dimensionality reduction.
Zhenhua Wang, Suling Liang, Lizhi Xu, Wei Song, Dexing Wang, Dongmei Huang   +5 more
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Non-Redundant Spectral Dimensionality Reduction

, 2017
Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization.
A Brun, A Hyvärinen, A Hyvärinen, A Singer, B Schölkopf, C Jutten, CC Chang, DL Donoho, EA Nadaraya, G Guo, GS Watson, JB Tenenbaum, L Maaten Van Der, M Belkin, M Belkin, M Rubinstein, MS Bartlett, N Halko, P Isola, RR Coifman, ST Roweis, X Geng, X He, Y Goldberg, Y LeCun, Z Zhang, Z Zhang   +26 more
core   +1 more source

Non-negative Dimensionality Reduction for Mammogram Classification [PDF]

Journal of Electrical and Electronics Engineering, 2009
Directly classifying high dimensional datamay exhibit the ``curse of dimensionality'' issue thatwould negatively influence the classificationperformance with an increase in the computationalload, depending also on the classifier structure.
I. Buciu, A. Gacsadi
doaj  

Quantum resonant dimensionality reduction

Physical Review Research
Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage.
Fan Yang, Furong Wang, Xusheng Xu, Pan Gao, Tao Xin, ShiJie Wei, Guilu Long   +6 more
doaj   +1 more source

Linear Dimensionality Reduction: What Is Better?

Data
This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’
Mohit Baliyan, Evgeny M. Mirkes
doaj   +1 more source