DeepAI
Log In Sign Up

Effective Data-aware Covariance Estimator from Compressed Data

10/10/2020
by   Xixian Chen, et al.
0

Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted sampling based covariance matrix estimator, namely DACE, which can provide an unbiased covariance matrix estimation and attain more accurate estimation under the same compression ratio. Moreover, we extend our proposed DACE to tackle multiclass classification problems with theoretical justification and conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superior performance of our DACE.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/13/2019

Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices

We propose a novel estimation approach for the covariance matrix based o...
10/29/2020

Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix

Estimating and storing the covariance (or correlation) matrix of high-di...
04/30/2019

A Low-Complexity Antenna-Layout-Aware Spatial Covariance Matrix Estimation Method

This paper proposed a low-complexity antenna layout-aware (ALA) covarian...
05/30/2019

Efficient Covariance Estimation from Temporal Data

Estimating the covariance structure of multivariate time series is a fun...
01/18/2019

Differentially Private High Dimensional Sparse Covariance Matrix Estimation

In this paper, we study the problem of estimating the covariance matrix ...
06/05/2020

Reliable Covariance Estimation

Covariance or scatter matrix estimation is ubiquitous in most modern sta...