1 citations · 1 across the 2 of their papers we have counts for
4 papers
Oblivious subspace embeddings for compressed Tucker decompositions
Matthew Pietrosanu, Bei Jiang, Linglong Kong
Emphasis in the tensor literature on random embeddings (tools for low-distortion dimension reduction) for the canonical polyadic (CP) tensor decomposition has left analogous result…
Advanced Algorithms for Penalized Quantile and Composite Quantile Regression
Matthew Pietrosanu, Jueyu Gao, Linglong Kong +2
In this paper, we discuss a family of robust, high-dimensional regression models for quantile and composite quantile regression, both with and without an adaptive lasso penalty for…
An Alternative Approach to Functional Linear Partial Quantile Regression
Dengdeng Yu, Matthew Pietrosanu, Ivan Mizera +3
Functional data such as curves and surfaces have become more and more common with modern technological advancements. The use of functional predictors remains challenging due to its…
The analysis of topological structure in data using persistent homology: applications to lexical word association networks
Matthew Pietrosanu
Persistent homology is a technique recently developed in algebraic and computational topology well-suited to analysing structure in complex, high-dimensional data. In this paper, w…