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Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions [PDF]

PeerJ Computer Science
Dimensionality reduction (DR) simplifies complex data from genomics, imaging, sensors, and language into interpretable forms that support visualization, clustering, and modeling.
Aasim Ayaz Wani
doaj   +3 more sources

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

European Journal of Remote Sensing, 2018
Managing transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary.
Giorgio Licciardi, Jocelyn Chanussot
doaj   +2 more sources

Convolutional 2D LDA for Nonlinear Dimensionality Reduction [PDF]

International Joint Conference on Artificial Intelligence, 2017
Qi Wang, Zequn Qin, Feiping Nie, Yuan Yuan   +3 more
openalex   +2 more sources

Automatic Computation of Left Ventricular Volume Changes Over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction [PDF]

J Digit Imaging, 2015
Z. Sani, Ahmad Shalbaf, H. Behnam, R. Shalbaf   +3 more
semanticscholar   +2 more sources

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Advances in Mathematical Physics, 2021
Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their ...
Yongbin Liu, Jingjie Wang, Wei Bai
doaj   +1 more source

An Orthogonal Locality and Globality Dimensionality Reduction Method Based on Twin Eigen Decomposition

IEEE Access, 2021
Dimensionality reduction is a hot research topic in pattern recognition. Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.
Shuzhi Su, Gang Zhu, Yanmin Zhu
doaj   +1 more source

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering [PDF]

Magnetic Resonance in Medicine, 2015
Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic
Guang Yang, F. Raschke, T. Barrick, F. Howe   +3 more
semanticscholar   +3 more sources

Evaluating Effectiveness of Nonlinear Dimensionality Reduction in Hedge Funds’ Returns Forecasting [PDF]

Annals of computer science and information systems
Milica Zukanović, Aleksa Radosavčević, Ana Poledica, Pavle Milošević, Ivan Luković   +4 more
doaj   +2 more sources

Nonlinear dimensionality reduction for the acoustic field measured by a linear sensor array [PDF]

MATEC Web of Conferences, 2019
Dimensionality reduction is one of the central problems in machine learning and pattern recognition, which aims to develop a compact representation for complex data from high-dimensional observations.
Zhang Xinyao, Wang Pengyu, Wang Ning
doaj   +1 more source

Temporal nonlinear dimensionality reduction [PDF]

The 2011 International Joint Conference on Neural Networks, 2011
Existing Nonlinear dimensionality reduction (NLDR) algorithms make the assumption that distances between observations are uniformly scaled. Unfortunately, with many interesting systems, this assumption does not hold. We present a new technique called Temporal NLDR (TNLDR), which is specifically designed for analyzing the high-dimensional observations ...
Mike Gashler, Tony Martinez
openaire   +1 more source