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Found 6 papers, 2 papers with code
Knowledge Transfer across Multiple Principal Component Analysis Studies
In the first step, we integrate the shared subspace information across multiple studies by a proposed method named as Grassmannian barycenter, instead of directly performing PCA on the pooled dataset.
Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis
We construct a Median-of-Means (MOM) estimator for the centered log-ratio covariance matrix and propose a thresholding procedure that is adaptive to the variability of individual entries.
Methodology
Projected Estimation for Large-dimensional Matrix Factor Models
In this study, we propose a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues.
Methodology
Large-dimensional Factor Analysis without Moment Constraints
Large-dimensional factor model has drawn much attention in the big-data era, in order to reduce the dimensionality and extract underlying features using a few latent common factors.
Methodology
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
Theoretically, we develop a theory for testing the equality of U-statistic based correlation matrices.
