Results 61 to 70 of about 5,853,511 (292)
Electronic Proceedings in Theoretical Computer Science, 2022 In Proceedings ACT 2021, arXiv:2211 ...
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Unsupervised Learning of Shape Manifolds [PDF]
Procedings of the British Machine Vision Conference 2007, 2007 Classical shape analysis methods use principal component analysis to reduce the dimensionality of shape spaces. The basic assumption behind these methods is that the subspace corresponding to the major modes of variation for a particular class of shapes is linearised. This may not necessarily be the case in practice.
Rajpoot, Nasir M. (Nasir Mahmood) +2 more
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Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells
Molecular Oncology, EarlyView.Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son +13 more
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Spectral Convergence of the connection Laplacian from random samples
, 2015 Spectral methods that are based on eigenvectors and eigenvalues of discrete graph Laplacians, such as Diffusion Maps and Laplacian Eigenmaps are often used for manifold learning and non-linear dimensionality reduction.
Singer, Amit, Wu, Hau-tieng
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IISE Transactions, 2020 As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis.
Chenang Liu +4 more
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Causal Learning via Manifold Regularization
Journal of machine learning research : JMLR, 2016 This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as `labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some
Hill, Steven M +3 more
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Manifold Learning and Nonlinear Homogenization
Multiscale Modeling & Simulation, 2022 We describe an efficient domain decomposition-based framework for nonlinear multiscale PDE problems. The framework is inspired by manifold learning techniques and exploits the tangent spaces spanned by the nearest neighbors to compress local solution manifolds.
Shi Chen +3 more
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Next‐generation proteomics improves lung cancer risk prediction
Molecular Oncology, EarlyView.This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj +4 more
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Review on graph learning for dimensionality reduction of hyperspectral image
Geo-spatial Information Science, 2020 Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction
Liangpei Zhang, Fulin Luo
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Simplicial Nonlinear Principal Component Analysis [PDF]
, 2012 We present a new manifold learning algorithm that takes a set of data points lying on or near a lower dimensional manifold as input, possibly with noise, and outputs a simplicial complex that fits the data and the manifold.
Hunt, Thomas, Krener, Arthur J.
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