Results 61 to 70 of about 478,588 (342)
Echo State Networks: analysis, training and predictive control
, 2019 The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a condition guaranteeing incremetal
albertini +12 more
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Dimensionality Reduction of Collective Motion by Principal Manifolds [PDF]
Physica-D : Nonlinear Phenomena, Volume 291, 15 January 2015,
Pages 62-73, 2015 While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods are not amenable to the analysis of such manifolds. This is mainly due to the necessary spectral decomposition step, which limits control over the mapping from the original high ...
arxiv +1 more source
Distortion-Free Nonlinear Dimensionality Reduction [PDF]
, 2008 Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method ...
Yangqing Jia +2 more
openaire +2 more sources
Nonlinear manifold learning for model reduction in finite elastodynamics [PDF]
, 2020 Model reduction in computational mechanics is generally addressed with linear dimensionality reduction methods such as Principal Components Analysis (PCA).
Arroyo, Marino, Zurita, D.
core +1 more source
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 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
Journal of Imaging, 2022 Dimensionality reduction techniques are often used by researchers in order to make high dimensional data easier to interpret visually, as data visualization is only possible in low dimensional spaces. Recent research in nonlinear dimensionality reduction
Liliya A. Demidova, Artyom V. Gorchakov
doaj +1 more source
Spline Embedding for Nonlinear Dimensionality Reduction [PDF]
, 2006 This paper presents a new algorithm for nonlinear dimensionality reduction (NLDR). Smoothing splines are used to map the locally-coordinatized data points into a single global coordinate system of lower dimensionality. In this work setting, we can achieve two goals.
Changshui Zhang +3 more
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A new method for performance analysis in nonlinear dimensionality reduction [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2020 AbstractIn this paper, we develop a local rank correlation (LRC) measure which quantifies the performance of dimension reduction methods. The LRC is easily interpretable, and robust against the extreme skewness of nearest neighbor distributions in high dimensions. Some benchmark datasets are studied.
Jiaxi Liang +2 more
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The Shape and Dimensionality of Phylogenetic Tree-Space Based on Mitochondrial Genomes [PDF]
, 2010 Phylogenetic analyses of large and diverse data sets generally result in large sets of competing phylogenetic trees. Consensus tree methods used to summarize sets of competing trees discard important information regarding the similarity and distribution ...
James C. Wilgenbusch +2 more
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