DeepAI AI Chat
Log In Sign Up

CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets

12/20/2019
by   Zhe Qu, et al.
22

A representative model in integrative analysis of two high-dimensional data types is to decompose each data matrix into a low-rank common matrix generated by latent factors shared across data types, a low-rank distinctive matrix corresponding to each data type, and an additive noise matrix. Existing decomposition methods claim that their common matrices capture the common pattern of the two data types. However, their so-called common pattern only denotes the common latent factors but ignores the common information between the two coefficient matrices of these latent factors. We propose a novel method, called the common and distinctive pattern analysis, which appropriately defines the two patterns by further incorporating the common and distinctive information of the coefficient matrices. A consistent estimation approach is developed for high-dimensional settings, and shows reasonably good finite-sample performance in simulations. We illustrate the superiority of proposed method over the state-of-the-art by real-world data examples obtained from Human Connectome Project and The Cancer Genome Atlas.

READ FULL TEXT

page 18

page 23

01/09/2020

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multiple High-dimensional Datasets

Modern biomedical studies often collect multiple types of high-dimension...
04/28/2019

Low-Rank Principal Eigenmatrix Analysis

Sparse PCA is a widely used technique for high-dimensional data analysis...
01/07/2020

Statistical Inference for High-Dimensional Matrix-Variate Factor Model

This paper considers the estimation and inference of factor loadings, la...
05/27/2021

Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence

We propose an estimator for the singular vectors of high-dimensional low...
06/27/2023

A new classification framework for high-dimensional data

Classification is a classic problem but encounters lots of challenges wh...
05/02/2019

High dimensional VAR with low rank transition

We propose a vector auto-regressive (VAR) model with a low-rank constrai...
04/16/2022

A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data

High-dimensional and sparse (HiDS) matrices are omnipresent in a variety...

Code Repositories

CDPA

CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets


view repo