Probabilistic PCA（PPCA）和Factor Analysis（FA）都是降维方法，且都基于潜在变量模型，但它们在误差项...
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_...
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 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. ...
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...
影响煤层吸附一氧化碳因素的主成分分析 Factors Affecting Coal Adsorption of Carbon Monoxide Principal Component Analysis Assessment of the effect on technical efficiency of bad loans in banking industry a principal component analysis and neuro-fuzzy system 基于两被联件振动信号概率密度和 PCA 的螺栓松动识别...
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...
Their method effectively removes the influence of temperature, train type, and speed from the damage-sensitive features using Multiple Linear Regression (MLR) and Principal Component Analysis (PCA). However, the main challenge with their proposed method is the manual extraction of damage-sensitive ...
[23]. To ensure a fair comparison, PCA was also applied to the regions detected by each method. We observe that incorporating a dimension reduction step results in all methods achieving clustering performance comparable to that obtained using genome-wide information, despite the considerably smaller ...
Model-based methods such as STRUCTURE [3] and ADMIXTURE [4] provide maximum likelihood estimations of ancestry based on ancestry proportions and allele frequencies but do not provide the simple 2D maps that can be obtained with PCA, multidimensional scaling (MDS), and other multivariate analysis ...