Protein Interaction Prediction Method Based on Feature Engineering and XGBoost [PDF]
BIO Web of Conferences, 2023 Human protein interaction prediction studies occupy an important place in systems biology. The understanding of human protein interaction networks and interactome will provide important insights into the regulation of developmental, physiological and ...
Zhao Xiaoman, Wang Xue
doaj +1 more source
Parallel t-SNE Applied to Data Visualization in Smart Cities
IEEE Access, 2020 The growth of smart city applications is increasingly around the world, many cities invest in the development of these systems intending to improve the management and life of their residents.
Maximiliano Araujo Da Silva Lopes +2 more
doaj +1 more source
Ensembles of Random Projections for Nonlinear Dimensionality Reduction [PDF]
, 2017 Dimensionality reduction methods are widely used in informationprocessing systems to better understand the underlying structuresof datasets, and to improve the efficiency of algorithms for bigdata applications.
Ghodsi, Ali +3 more
core +2 more sources
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings
, 2018 The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years.
Ornek, Cem, Vural, Elif
core +1 more source
A HJB-POD approach for the control of nonlinear PDEs on a tree structure [PDF]
, 2019 The Dynamic Programming approach allows to compute a feedback control for nonlinear problems, but suffers from the curse of dimensionality. The computation of the control relies on the resolution of a nonlinear PDE, the Hamilton-Jacobi-Bellman equation ...
Alla, Alessandro, Saluzzi, Luca
core +3 more sources
Noncommutative reduction of the nonlinear Schrödinger equation on Lie groups [PDF]
arXiv, 2021 We propose a new approach that allows one to reduce nonlinear equations on Lie groups to equations with a fewer number of independent variables for finding particular solutions of the nonlinear equations. The main idea is to apply the method of noncommutative integration to the linear part of a nonlinear equation, which allows one to find bases in the ...
arxiv
Dimensional reduction in nonlinear filtering: A homogenization approach
The Annals of Applied Probability, 2013 Published in at http://dx.doi.org/10.1214/12-AAP901 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Imkeller, Peter +3 more
openaire +4 more sources
3D-2D dimensional reduction for a nonlinear optimal design problem with perimeter penalization [PDF]
, 2012 A 3D-2D dimension reduction for a nonlinear optimal design problem with a perimeter penalization is performed in the realm of $\Gamma$-convergence, providing an integral representation for the limit functional.
arxiv +1 more source
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks [PDF]
arXiv, 2021 Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality. High-dimensional data are usually in a coherent structure and make the data in relatively small true degrees of freedom. There are global and local dimensionality reduction methods to alleviate the problem.
arxiv
Nonlinear Dimensionality Reduction for Face Recognition [PDF]
, 2009 Principal component analysis (PCA) has long been a dominating linear technique for dimensionality reduction. Many nonlinear methods and neural networks have been proposed to extend PCA for complex nonlinear data. They include kernel PCA, local linear embedding, isomap, self-organising map (SOM), and visualization induced SOM (ViSOM), a variant of SOM ...
Hujun Yin, Weilin Huang
openaire +2 more sources