Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2020.1856119
Abstract: Abstract Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal component analysis is a linear dimensionality reduction method in the sense… read more here.
Keywords: forward stepwise; dimensionality; dimensionality reduction; stepwise deep ... See more keywords
Classifying FRB spectrograms using nonlinear dimensionality reduction techniques
Sign Up to like & getrecommendations! Published in 2023 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/stad1304
Abstract: Fast radio bursts (FRBs) are mysterious astronomical phenomena, and it is still uncertain whether they consist of multiple types. In this study we use two nonlinear dimensionality reduction algorithms - Uniform Manifold Approximation and Projection… read more here.
Keywords: apparently non; repeaters apparently; dimensionality reduction; nonlinear dimensionality ... See more keywords
Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Physiology"
DOI: 10.3389/fphys.2020.614565
Abstract: In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel… read more here.
Keywords: pattern; time; ordinal pattern; dimensionality reduction ... See more keywords