7 citations · 9 across the 5 of their papers we have counts for
5 papers
Highly robust factored principal component analysis for matrix-valued outlier accommodation and explainable detection via matrix minimum covariance determinant
Wenhui Wu, Changchun Shang, Jianhua Zhao +2
Principal component analysis (PCA) is a classical and widely used method for dimensionality reduction, with applications in data compression, computer vision, pattern recognition,…
Matrix Healy Plot: A Practical Tool for Visual Assessment of Matrix-Variate Normality
Fen Jiang, Jianhua Zhao, Changchun Shang +3
Matrix-valued data, where each observation is represented as a matrix, frequently arises in various scientific disciplines. Modeling such data often relies on matrix-variate normal…
Robust bilinear factor analysis based on the matrix-variate distribution
Xuan Ma, Jianhua Zhao, Changchun Shang +2
Factor Analysis based on multivariate distribution (fa) is a useful robust tool for extracting common factors on heavy-tailed or contaminated data. However, fa is only ap…
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion
Jianhua Zhao, Changchun Shang, Shulan Li +2
The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size , is a popular model selection criterion for…
Robust factored principal component analysis for matrix-valued outlier accommodation and detection
Xuan Ma, Jianhua Zhao, Yue Wang
Principal component analysis (PCA) is a popular dimension reduction technique for vector data. Factored PCA (FPCA) is a probabilistic extension of PCA for matrix data, which can su…