Results 91 to 100 of about 5,853,511 (292)
Learning Smooth Pattern Transformation Manifolds [PDF]
IEEE Transactions on Image Processing, 2013 Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals.
Vural, Elif, Frossard, Pascal
openaire +3 more sources
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 +10 more
wiley +1 more source
Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders
Algorithms, 2019 The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations ...
Fayeem Aziz +2 more
doaj +1 more source
Manifold Constrained Low-Rank and Joint Sparse Learning for Dynamic Cardiac MRI
IEEE Access, 2020 Reconstruction from highly accelerated dynamic magnetic resonance imaging (MRI) is of great significance for medical diagnosis. The application of low-rank and sparse matrix decomposition to MRI can improve imaging speed and efficiency.
Qingmin Meng, Xianchao Xiu, Yan Li
doaj +1 more source
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
, 2019 Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently ...
Huang, Zhiwu, Van Gool, Luc, Wu, Jiqing
core +1 more source
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 +26 more
wiley +1 more source
Gaussian bandwidth selection for manifold learning and classification
Data mining and knowledge discovery, 2020 Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks.
Ofir Lindenbaum +3 more
semanticscholar +1 more source
Learning 3-manifold triangulations
Journal of Physics A: Mathematical and TheoreticalAbstract Real 3-manifold triangulations can be uniquely represented by isomorphism signatures. Databases of these isomorphism signatures are generated for a variety of 3-manifolds and knot complements, using SnapPy and Regina, then these language-like inputs are used to train various machine learning architectures to differentiate the ...
Costantino, F, He, Y, Heyes, E, Hirst, E
openaire +3 more sources
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source