这个能表征全部数据特征的全局模型就称为因子分析(Factor Analysis)。对于一个特例,ωω是正交矩阵,满足ωTω=I,ϵ=σ2IωTω=I,ϵ=σ2I,,此时模型就是概率性主成分分析PPCA,进一步地当σ2→0σ2→0时,就是通常意义上的PCA。 作者:scott198510 链接:PCA与PPCA推导及理解_scott198510的博客-CSDN博客_...
a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach to PCA. ...
This model is originated from factor analysis theory. The probability distributions using PPCA are well defined. In particular, GMM and PPCA are found to be equivalent when using diagonal covariance matrix. In this study, we derive a novel PPCA model selection and establish models for different...
A Probabilistic Interpretation of Canonical Correlation Analysis Review : probabilistic interpretation of PCA Probabilistic interpretation of CCA Definitio... Archived records of the GCM simulated fields are related to observed rainfall through a set of canonical correlation analysis (CCA) equations. Probabili...
& Wang, L. Groundwater quality in and around a landfill in Northwest China: Characteristic pollutant identification, health risk assessment, and controlling factor analysis. Expo. Health 14, 885–901. https://doi.org/10.1007/s12403-022-00464-6 (2022). Article CAS Google Scholar He, S., ...
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates ...
MFA: Mixture of Factor Analyzers is the more general model. MPPCA: In Mixture of Probabilistic PCA, the "noise" is isotropic i.e. all added diagonal elements in the covariance are the same. Additional reading: On GANs and GMMs paper Factor Analysis on Wikipedia MPPCA paper MFA paper TODO...
Multivariate analysis methods such as correlation analysis, cluster analysis (CA), and principal component analysis (PCA) are often used to trace soil pollution sources (Jamshidi and Saeedi, 2013). Pearson correlation analysis was performed on soil heavy metal content, and the correlation coefficients...
(purple color) is performed with three models: Group Factor Analysis (GFA), sparse factor analysis (sFA), and Bayesian PCA (BPCA). These are compared to two earlier connectivity mapping methods (orange color): rank-based average enrichment-score distance (AESD) and the Pearson correlation ...
We suggest the use of generalized PCA with suitable metric M as a Projection Pursuit technique. According to the kind of structure which is looked for, two such metrics are proposed in section 3. Finally, the analysis of n脳p contingency tables is considered in section 4. Since the data ...