Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate

TL;DR

This paper introduces pSCOPE, a distributed sparse learning method that leverages data partition quality to achieve faster convergence and improved efficiency in high-dimensional machine learning tasks.

Contribution

The paper proposes pSCOPE, a novel distributed sparse learning algorithm that incorporates a data partition quality metric to enhance convergence speed and efficiency.

Findings

pSCOPE achieves linear convergence with good data partitions.

Better data partitioning leads to faster convergence.

Experimental results outperform state-of-the-art methods.

Abstract

Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use $L_1$ regularization. In this paper, we propose a novel method, called proximal \mbox{SCOPE}~(\mbox{pSCOPE}), for distributed sparse learning with $L_1$ regularization. pSCOPE is based on a \underline{c}ooperative \underline{a}utonomous \underline{l}ocal \underline{l}earning~(\mbox{CALL}) framework. In the \mbox{CALL} framework of \mbox{pSCOPE}, we find that the data partition affects the convergence of the learning procedure, and subsequently we define a metric to measure the goodness of a data partition. Based on the defined metric, we theoretically prove that pSCOPE is convergent with a linear convergence rate if the data partition is good enough. We also prove that better data partition implies faster convergence rate. Furthermore, pSCOPE is also communication efficient. Experimental results on real data sets show that pSCOPE can outperform other state-of-the-art distributed methods for sparse learning.