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 ...

The conventional view of PCA is a geometric one, finding a low-dimensional projection that minimizes the squared-error loss. An alternate view is a probabilistic one ...

This project is an implementation of Principal Component Analysis (PCA) in Python. PCA is a technique for dimensionality reduction and data visualization that aims to find the most important ...

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 ...

Abstract: 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 ...

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 ...