Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by ...

Algorithms for conventional PCA are guaranteed to converge to a unique answer independent of initialization. In general, E-PCA does not have this property: the loss function (8) may have multiple ...

PCA is a technique for dimensionality reduction and data visualization that aims to find the most important underlying patterns in a dataset. python machine-learning analysis python-implementation ...

Aiming at the problem that traditional clustering algorithms cannot adapt to spatiotemporal data mining, this paper proposes a new clustering algorithm PCA-Kmeans++. First, in order to reduce the ...

Keywords: single-cell sequencing, robust principal component analysis, data denoising, clustering, tree structure reconstruction Citation: Chen Z, Zhang B, Gong F, Wan L and Ma L (2023) RobustTree: An ...

PCA algorithm is a typical data dimensionality reduction method, which projects high-dimensional data to a lower-dimensional space to obtain a low-dimensional data set that can maximally represent ...

Abstract We proposed a generalized adaptive learning rate (GALR) PCA algorithm, which could be guaranteed that the algorithm’s convergence process would not be affected by the selection of the initial ...