Results 301 to 310 of about 493,672 (330)

Supervised Dimensionality Reduction via Nonlinear Target Estimation

2013
Dimensionality reduction is a crucial ingredient of machine learning and data mining, boosting classification accuracy through the isolation of patterns via omission of noise. Nevertheless, recent studies have shown that dimensionality reduction can benefit from label information, via a joint estimation of predictors and target variables from a low ...
Grabocka J., Drumond L., Schmidt-Thieme L.   +2 more
openaire   +1 more source

Nonlinear dimensionality reduction by curvature minimization

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
In this paper, we introduce a nonlinear dimensionality reduction (NLDR) technique that can construct a low-dimensional embedding efficiently and accurately with low embedding distortions. The key idea is to divide NLDR into nonlinearity reduction and linear dimensionality reduction, which simplifies the overall NLDR process.
Yusuke Yoshiyasu, Eiichi Yoshida
openaire   +1 more source

A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and Nonlinear Dimensionality Reduction (tSNE or UMAP).

Methods in molecular biology, 2023
T. Ujas, Veronica Obregon-Perko, Ann M. Stowe   +2 more
semanticscholar   +1 more source

Linear versus Nonlinear Dimensionality Reduction of High-Dimensional Dynamical Systems

SIAM Journal on Scientific Computing, 2004
The author uses combinations of the K-L decomposition and neural networks to obtain the intrinsic or true dimension of two PDEs, namely, the 1-d K-S equation and the 2-d N-S equations. For the 1-d K-S equation, he investigates one particular dynamical behavior which, in phase space, is represented by a heteroclinic connection.
openaire   +1 more source

Supervised Nonlinear Dimensionality Reduction for Visualization and Classification

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2005
When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques. However, when Isomap is applied to real-world data, it shows some limitations, such as being sensitive to noise. In this paper, an improved version of Isomap,
Xin, Geng, De-Chuan, Zhan, Zhi-Hua, Zhou
openaire   +2 more sources

Dimensional Reduction of Nonlinear Delay Systems

2002
Time delays usually give rise to great difficulty in the dynamic analysis of controlled mechanical systems. The difficulty increases so dramatically with an increase of system dimensions that the analytical results for the dynamics of delay systems of high dimensions are considerably few.
Haiyan Hu, Zaihua Wang
openaire   +1 more source

Some aspects of nonlinear dimensionality reduction

Computational Statistics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liwen Wang, Yongda Wang, Shifeng Xiong, Jiankui Yang   +3 more
openaire   +1 more source

Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques

Expert systems with applications, 2020
N. Rabin, M. Kahlon, Sarit Malayev, A. Ratnovsky   +3 more
semanticscholar   +1 more source

Dimensionality reduction made high-performance mid-infrared nonlinear halide crystal

Materials today physics, 2021
Qi Wu, Can Yang, Xian Liu, Jie Ma, Fei Liang, Yeshuang Du   +5 more
semanticscholar   +1 more source

Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

MLSDA'14, 2014
M. Sakurada, T. Yairi
semanticscholar   +1 more source