Results 81 to 90 of about 493,672 (330)

Manifold Approximation by Moving Least-Squares Projection (MMLS)

, 2019
In order to avoid the curse of dimensionality, frequently encountered in Big Data analysis, there was a vast development in the field of linear and nonlinear dimension reduction techniques in recent years.
Levin, David, Sober, Barak
core   +1 more source

Unraveling the Molecular Mechanisms of Glioma Recurrence: A Study Integrating Single‐Cell and Spatial Transcriptomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu, Yinjiao Fei, Jiaxuan Ding, Kexin Shi, Jinyan Luo, Yuchen Zhu, Xingjian Sun, Gefei Jiang, Yuandong Cao, Weilin Xu, Shu Zhou   +10 more
wiley   +1 more source

A New Approach to Improve the Topological Stability in Non-Linear Dimensionality Reduction

IEEE Access, 2020
Dimensionality reduction in the machine learning field mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data.
Mohammed Elhenawy, Mahmoud Masoud, Sebastien Glaser, Andry Rakotonirainy   +3 more
doaj   +1 more source

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

Locality constrained dictionary learning for non‐linear dimensionality reduction and classification

IET Computer Vision, 2017
In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study.
Lina Liu, Shiwei Ma, Ling Rui, Jian Lu
doaj   +1 more source

Hip Morphology–Based Osteoarthritis Risk Prediction Models: Development and External Validation Using Individual Participant Data From the World COACH Consortium

Arthritis Care &Research, EarlyView.
Objective This study aims to develop hip morphology‐based radiographic hip osteoarthritis (RHOA) risk prediction models and investigates the added predictive value of hip morphology measurements and the generalizability to different populations. Methods We combined data from nine prospective cohort studies participating in the Worldwide Collaboration ...
Myrthe A. van den Berg, Fleur Boel, Michiel M. A. van Buuren, Noortje S. Riedstra, Jinchi Tang, Harbeer Ahedi, Nigel K. Arden, Sita M. A. Bierma‐Zeinstra, Cindy G. Boer, Flavia M. Cicuttini, Timothy F. Cootes, David T. Felson, Willem Paul Gielis, Joshua Heerey, Graeme Jones, Stefan Kluzek, Nancy E. Lane, Claudia Lindner, Joyce B. J. van Meurs, Andrea Mosler, Amanda E. Nelson, Michael C. Nevitt, Edwin H. Oei, Jos Runhaar, Harrie Weinans, Jesse H. Krijthe, Rintje Agricola   +26 more
wiley   +1 more source

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

Hopfield Neural Networks for Online Constrained Parameter Estimation With Time‐Varying Dynamics and Disturbances

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley   +1 more source

Low‐rank isomap algorithm

IET Signal Processing, 2022
Isomap is a well‐known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete ...
Eysan Mehrbani, Mohammad Hossein Kahaei
doaj   +1 more source

Visualizing dimensionality reduction of systems biology data

, 2012
One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a
A Hyvaerinen, A Hyvaerinen, A Inselberg, A Inselberg, A Saeed, Albert Pritzkau, Andreas Lehrmann, Aydin C. Polatkan, DH Jeong, DJ Lockhart, F Battke, F Battke, GH Golub, H Abdi, H Hotelling, HF Kaiser, J Shendure, JB Tenenbaum, K Pearson, Kay Nieselt, KQ Weinberger, LK Saul, M Fontes, M Harrower, M Schena, Michael Huber, P Mannfolk, R Karbauskaite, R Tarjan, S Roweis, Z Zhang, Ö Altug-Teber   +31 more
core   +1 more source