Distributed sparse diffusion estimation with reduced communication cost
نویسندگان
چکیده
منابع مشابه
On Communication Cost of Distributed Statistical Estimation and Dimensionality
We explore the connection between dimensionality and communication cost in distributed learning problems. Specifically we study the problem of estimating the mean ~ ✓ of an unknown d dimensional gaussian distribution in the distributed setting. In this problem, the samples from the unknown distribution are distributed among m different machines. The goal is to estimate the mean ~ ✓ at the optim...
متن کاملDistributed Mean Estimation with Limited Communication
Motivated by the need for distributed learning and optimization algorithms with low communication cost, we study communication efficient algorithms for distributed mean estimation. Unlike previous works, we make no probabilistic assumptions on the data. We first show that for d dimensional data with n clients, a naive stochastic rounding approach yields a mean squared error (MSE) of ⇥(d/n) and ...
متن کاملProbabilistic ODF Estimation from Reduced HARDI Data with Sparse Regularization
High Angular Resolution Diffusion Imaging (HARDI) demands a higher amount of data measurements compared to Diffusion Tensor Imaging (DTI), restricting its use in practice. We propose to represent the probabilistic Orientation Distribution Function (ODF) in the frame of Spherical Wavelets (SW), where it is highly sparse. From a reduced subset of measurements (nearly four times less than the stan...
متن کاملRobust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملCommunication-efficient Distributed Sparse Linear Discriminant Analysis
We propose a communication-e cient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size N into m machines, and estimates a local sparse LDA estimator on each machine using the data subset of size N/m. After the distributed estimation, our method aggregates the debiased local estimators from m machines...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IET Signal Processing
سال: 2018
ISSN: 1751-9675,1751-9683
DOI: 10.1049/iet-spr.2017.0377