8 citations · 12 across the 6 of their papers we have counts for
6 papers
Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance
Hsin-Hsiung Huang, Feng Yu, Kang Li +1
We propose a novel Fréchet sufficient dimension reduction (SDR) method based on kernel distance covariance, tailored for metric space-valued responses such as count data, probabili…
A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion
Feng Yu, Teng Zhang, Gilad Lerman
We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers. STE is a…
Robust Sufficient Dimension Reduction via -Distance Covariance
Hsin-Hsiung Huang, Feng Yu, Teng Zhang
We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using -distance covariance (dCov) in dimension-reduction problems. Under very m…
Robust Regularized Low-Rank Matrix Models for Regression and Classification
Hsin-Hsiung Huang, Feng Yu, Xing Fan +1
While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient esti…
ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network
Xing Fan, Marianna Pensky, Feng Yu +1
The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped w…
An Algorithm for Graph-Fused Lasso Based on Graph Decomposition
Feng Yu, Yi Yang, Teng Zhang
This work proposes a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally…