Search Results for author:
Found 10 papers, 0 papers with code
Covariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures
We propose a flexible dual functional factor model for modelling high-dimensional functional time series.
Linear Discriminant Analysis with High-dimensional Mixed Variables
Datasets containing both categorical and continuous variables are frequently encountered in many areas, and with the rapid development of modern measurement technologies, the dimensions of these variables can be very high.
DECOrrelated feature space partitioning for distributed sparse regression
The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space).
A Direct Approach for Sparse Quadratic Discriminant Analysis
Formulated in a simple and coherent framework, DA-QDA aims to directly estimate the key quantities in the Bayes discriminant function including quadratic interactions and a linear index of the variables for classification.
No penalty no tears: Least squares in high-dimensional linear models
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.
High-dimensional Ordinary Least-squares Projection for Screening Variables
Variable selection is a challenging issue in statistical applications when the number of predictors $p$ far exceeds the number of observations $n$.
On the consistency theory of high dimensional variable screening
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection.
Convex Optimization Procedure for Clustering: Theoretical Revisit
In this paper, we present theoretical analysis of SON~--~a convex optimization procedure for clustering using a sum-of-norms (SON) regularization recently proposed in \cite{ICML2011Hocking_419, SON, Lindsten650707, pelckmans2005convex}.
Provable Subspace Clustering: When LRR meets SSC
Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both considered as the state-of-the-art methods for {\em subspace clustering}.
Gradient-based kernel method for feature extraction and variable selection
We propose a novel kernel approach to dimension reduction for supervised learning: feature extraction and variable selection; the former constructs a small number of features from predictors, and the latter finds a subset of predictors.
