doi:10.1029/2024SW004185John Wiley & Sons, LtdSpace WeatherPiyush M. MehtaDepartment of Mechanical Materials and Aerospace Engineering West Virginia University Morgantown WV USARichard J. LicataDepartment of Mechanical Materials and Aerospace Engineering West Virginia University Morgantown WV USA...
Single-cell RNA sequencing (scRNA-seq) is an ideal approach for investigating cell-cell variation. Conventional dimensionality reduction techniques such as principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE)1 were implemented on scRNA-seq data for visualization and...
One of the most common dimensionality reduction technique is filtering, in which you leave most of the dimensions and concentrate only on certain dimensions. But that doesn’t always work, when you are dealing with image data, the number of pixels represents the number of dimensions in the imag...
, distributed, and gradient architecture implemented in a shared vector-space model that employs compression through routinization and dimensionality reduction. ... MT Putnam,C Matthew,R David - 《Frontiers in Psychology》 被引量: 2发表: 2017年 Exploring Dimensionality Reduction Techniques in Multiling...
This thesis investigates the use of sequential parametric projection pursuits (SPPP) for hyperspectral dimensionality reduction and invasive species target recognition. The SPPP method is implemented in a top-down fashion, where hyperspectral bands are used to form an increasing number of sm...
In the case of supervised learning, dimensionality reduction can be used to simplify the features fed into the machine learning classifier. The most common methods used to carry out dimensionality reduction for supervised learning problems is Linear Discriminant Analysis (LDA) and PCA, and it can be...
an algorithm for detecting ring structures in the manifold is constructed and a nonlinear dimensionality reduction strategy is developed through polar coordinates transformation.A series of simulation results implemented on a series of synthetic and real-world data sets generated by manifolds with or ...
variables that may be more informative compared with a given individual metabolite. Common dimensionality reduction methods include: (i) unsupervised methods, such as PCA or factor analysis, where the algorithm is not informed of the outcome; (ii) supervised methods, such as partial least squares-...
Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood. J Comput Grap Statist. 2017;26:826-839.Sobczyk, P., Bogdan, M. and Josse, J. Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood. 2016. [Cited: May 04, 2018.] https://arxiv...
We demonstrate that, for a given initial and targetsuperposition, it is always possible to choose the laser pulses so that in atransformed basis the system is reduced to an effective three-level $\\Lambda$system, and standard STIRAP processes can be implemented. Our treatment leadsto a simple...