Results 1 to 10 of about 115,342 (279)

Video Analysis Via Nonlinear Dimensionality Reduction [PDF]

, 2007
In this work we present an application of nonlinear dimensionality reduction techniques for video analysis. We review several methods for dimensionality reduction and then concentrate on the study of Diffusion Maps. First we show how diffusion maps can be applied to video analysis.
openaire   +1 more source

Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data—In Pursuit of Precision

Bioengineering
Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages.
Karthika M S, Harikumar Rajaguru, Ajin R. Nair   +2 more
doaj   +1 more source

A tractable latent variable model for nonlinear dimensionality reduction. [PDF]

Proc Natl Acad Sci U S A, 2020
Saul LK.
europepmc   +1 more source

Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities. [PDF]

Front Physiol, 2020
Kottlarz I, Berg S, Toscano-Tejeida D, Steinmann I, Bähr M, Luther S, Wilke M, Parlitz U, Schlemmer A.   +8 more
europepmc   +1 more source

Learning Propagators for Sea Surface Height Forecasts Using Koopman Autoencoders

Geophysical Research Letters
Due to the wide range of processes impacting the sea surface height (SSH) on daily‐to‐interannual timescales, SSH forecasts are hampered by numerous sources of uncertainty.
Andrew E. Brettin, Laure Zanna, Elizabeth A. Barnes   +2 more
doaj   +1 more source

Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization. [PDF]

BMC Bioinformatics, 2017
Yang J, Wang H, Ding H, An N, Alterovitz G.   +4 more
europepmc   +1 more source

Graph Embedding and Nonlinear Dimensionality Reduction

, 2011
Traditionally, spectral methods such as principal component analysis (PCA) have been applied to many graph embedding and dimensionality reduction tasks. These methods aim to find low-dimensional representations of data that preserve its inherent structure.
openaire   +2 more sources

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification. [PDF]

J Sci Comput
Zanoni A, Geraci G, Salvador M, Marsden AL, Schiavazzi DE.   +4 more
europepmc   +1 more source

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   +1 more source

Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers. [PDF]

Biosensors (Basel)
Kucukselbes H, Sayilgan E.
europepmc   +1 more source