Fierimonte Roberto, Scardapane Simone, Uncini Aurelio, Panella Massimo
IEEE Trans Neural Netw Learn Syst. 2017 Nov;28(11):2699-2711. doi: 10.1109/TNNLS.2016.2597444. Epub 2016 Aug 26.
Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.
分布式学习是指在训练数据分布于不同节点时推断函数的问题。虽然在监督学习和无监督学习的背景下已经开展了大量工作，但分布式环境下半监督学习这种中间情况受到的关注较少。在本文中，我们通过扩展流形正则化框架，针对此类问题提出了一种算法。所提算法的主要组成部分包括对训练模式邻接矩阵的完全分布式计算。为此，我们基于扩散自适应框架提出了一种用于低秩分布式矩阵补全的新颖算法。总体而言，分布式半监督算法高效且可扩展，并且通过纳入用于相似度计算的灵活隐私保护机制可以保护隐私。在一系列标准半监督基准上的实验结果和比较验证了我们的提议。
