6 papers
Favourable Missingness in Semi-Supervised Classification for Exponential Mixture Models
Huanchao Zhou, Jinran Wu, Fariborz Setoudehtazang +1
Semi-supervised classifiers are commonly trained from samples in which all features are observed but some class labels are missing. When label missingness is independent of the obs…
Exact Separation of Eigenvalues of Large Dimensional Noncentral Sample Covariance Matrices
Zhidong Bai, Jiang Hu, Jack W. Silverstein +1
Let $ \bbB_n =\frac{1}{n}(\bbR_n + \bbT^{1/2}_n \bbX_n)(\bbR_n + \bbT^{1/2}_n \bbX_n)^* $ where $ \bbX_n $ is a matrix with independent standardized random variables…
No Eigenvalues Outside the Support of the Limiting Spectral Distribution of Large Dimensional noncentral Sample Covariance Matrices
Zhidong Bai, Jiang Hu, Jack W. Silverstein +1
Let $ \bbB_n =\frac{1}{n}(\bbR_n + \bbT^{1/2}_n \bbX_n)(\bbR_n + \bbT^{1/2}_n \bbX_n)^* $, where $ \bbX_n $ is a matrix with independent standardized random variable…
Analysis of the limiting spectral distribution of large dimensional General information-plus-noise type matrices
Huanchao Zhou, Jiang Hu, Zhidong Bai +1
In this paper, we derive the analytical behavior of the limiting spectral distribution of non-central covariance matrices of the "general information-plus-noise" type, as studied i…
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
Hua Li, Wenya Luo, Zhidong Bai +2
This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectral…
The limiting spectral distribution of large dimensional general information-plus-noise type matrices
Huanchao Zhou, Zhidong Bai, Jiang Hu
Let be random complex matrices, and be non-random complex matrices with dimensions and , respectively. We assume that…