Results 81 to 90 of about 4,379,261 (288)
lcUMAPtSNE: use of non-linear dimensionality reduction techniques with genotype likelihoods
BMC GenomicsBackground Understanding population structure is essential for conservation genetics, as it provides insights into population connectivity and supports the development of targeted strategies to preserve genetic diversity and adaptability.
Kerem Uzel +2 more
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SIAM Journal on Mathematics of Data ScienceAbstract. Dimensionality reduction on quadratic manifolds augments linear approximations with quadratic correction terms. Previous works rely on linear approximations given by projections onto the first few leading principal components of the training data; however, linear approximations in subspaces spanned by the leading principal ...
Paul Schwerdtner, Benjamin Peherstorfer
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Multiple Manifold Learning by Nonlinear Dimensionality Reduction [PDF]
, 2011 Methods for nonlinear dimensionality reduction have been widely used for different purposes, but they are constrained to single manifold datasets. Considering that in real world applications, like video and image analysis, datasets with multiple manifolds are common, we propose a framework to find a low-dimensional embedding for data lying on multiple ...
Juliana Valencia-Aguirre +4 more
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Nonlinear methods for design-space dimensionality reduction in shape optimization [PDF]
, 2017 In shape optimization, design improvements significantly depend on the dimension and variability of the design space. High dimensional and variability spaces are more difficult to explore, but also usually allow for more significant improvements.
D'AGOSTINO, DANNY +11 more
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Advanced Engineering Materials, EarlyView.More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad +8 more
wiley +1 more source
Principal Tensor Embedding for Unsupervised Tensor Learning
IEEE Access, 2020 Tensors and multiway analysis aim to explore the relationships between the variables used to represent the data and find a summarization of the data with models of reduced dimensionality. However, although in this context a great attention was devoted to
Claudio Turchetti +2 more
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Advances in Sustainable and Wearable Textile Based Soft Robotics
Advanced Functional Materials, EarlyView.This Review examines advances in wearable textile‐based soft robotics, focusing on sustainable materials, integrated sensing, and scalable actuation. It discusses manufacturing and system integration across healthcare, assistive robotics, prosthetics, and human–machine interfaces, and highlights key challenges in circular design, including life‐cycle ...
Zahir Abbas +6 more
wiley +1 more source
Gaussian processes autoencoder for dimensionality reduction [PDF]
, 2014 Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing.
Cai, Z., Jiang, X., Hong, Xia, Gao, J.
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Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray
Sensors, 2008 Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples.
Lan Shu, Xuehua Li
doaj
Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
, 2005 We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix ...
Benjamin D. Packer +2 more
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