Dimension reduction techniques for the integrative analysis of multi-omics data
State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-
throughput 'omics' technologies enable the efficient generation of large experimental data …
throughput 'omics' technologies enable the efficient generation of large experimental data …
[图书][B] Longitudinal structural equation modeling: A comprehensive introduction
JT Newsom - 2023 - taylorfrancis.com
Longitudinal Structural Equation Modeling is a comprehensive resource that reviews
structural equation modeling (SEM) strategies for longitudinal data to help readers …
structural equation modeling (SEM) strategies for longitudinal data to help readers …
An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
An improved mixture of probabilistic principal component analysis (PPCA) has been
introduced for nonlinear data-driven process monitoring in this paper. To realize this …
introduced for nonlinear data-driven process monitoring in this paper. To realize this …
Directional PCA for fast detection and accurate diagnosis: A unified framework
J Li, D Ding, F Tsung - IEEE Transactions on Cybernetics, 2021 - ieeexplore.ieee.org
Many methods for monitoring multivariate processes are built on principal component
analysis (PCA), which, however, simply tells whether the process is faulty or not. In fact …
analysis (PCA), which, however, simply tells whether the process is faulty or not. In fact …
Class enumeration and parameter recovery of growth mixture modeling and second-order growth mixture modeling in the presence of measurement noninvariance …
Population heterogeneity in growth trajectories can be detected with growth mixture
modeling (GMM). It is common that researchers compute composite scores of repeated …
modeling (GMM). It is common that researchers compute composite scores of repeated …
Quartiles and Mel Frequency Cepstral Coefficients vectors in Hidden Markov-Gaussian Mixture Models classification of merged heart sounds and lung sounds signals
P Mayorga, D Ibarra, V Zeljkovic… - … Conference on High …, 2015 - ieeexplore.ieee.org
This paper presents integrated Hidden Markov and Gaussian Mixture Models (HMM-GMM)
to classify lung sounds (LS) and heart sounds (HS) characteristics. In order to optimize the …
to classify lung sounds (LS) and heart sounds (HS) characteristics. In order to optimize the …
Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion
The Bayesian information criterion (BIC), defined as the observed data log likelihood minus
a penalty term based on the sample size N, is a popular model selection criterion for factor …
a penalty term based on the sample size N, is a popular model selection criterion for factor …
Degradation feature extraction of the hydraulic pump based on high-frequency harmonic local characteristic-scale decomposition sub-signal separation and discrete …
J Sun, H Li, B Xu - Advances in Mechanical Engineering, 2016 - journals.sagepub.com
Hydraulic pump degradation feature extraction is a key step of condition-based
maintenance. In this article, a novel method based on high-frequency harmonic local …
maintenance. In this article, a novel method based on high-frequency harmonic local …
Robust bilinear factor analysis based on the matrix-variate distribution
Factor Analysis based on multivariate $ t $ distribution ($ t $ fa) is a useful robust tool for
extracting common factors on heavy-tailed or contaminated data. However, $ t $ fa is only …
extracting common factors on heavy-tailed or contaminated data. However, $ t $ fa is only …
An unsupervised multi-manifold discriminant isomap algorithm based on the pairwise constraints
X Gao, J Liang, W Wang, X Bai, L Jia - International Journal of Machine …, 2022 - Springer
In this paper, an unsupervised multi-manifold Isomap algorithm, which is named UMD-
Isomap, is proposed for the purpose of dimensionality reduction and clustering of multi …
Isomap, is proposed for the purpose of dimensionality reduction and clustering of multi …