Distributed data clustering over networks

Altilio, Rosa; Lorenzo, Paolo Di; Panella, Massimo

doi:10.1016/j.patcog.2019.04.021

Cited by 21 publications

(5 citation statements)

References 36 publications

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“…While these works are often formulated as generic optimization problems, rather than designed for a specific learning task, they tend to be motivated by applications where data is distributed by examples (horizontally partitioned), as is made clear, for example, in Forero et al (2010). Closer to our work, multiple fully decentralized algorithms use EM to fit GMMs in horizontally partitioned setups, such as Nowak (2003); Kowalczyk and Vlassis (2005); Gu (2008); Forero et al (2008); Bhaduri and Srivastava (2009);Safarinejadian et al (2010); Weng et al (2011); Altilio et al (2019). Related density estimation tasks have also been considered (Hu et al, 2007;Hua and Li, 2015;Dedecius and Djurić, 2017).…”

Section: Introductionmentioning

confidence: 87%

Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features

Valdeira¹,

Soares²

2022

Preprint

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Expectation Maximization (EM) is the standard method to learn Gaussian mixtures. Yet its classic, centralized form is often infeasible, due to privacy concerns and computational and communication bottlenecks. Prior work dealt with data distributed by examples, horizontal partitioning, but we lack a counterpart for data scattered by features, an increasingly common scheme (e.g. user profiling with data from multiple entities). To fill this gap, we provide an EM-based algorithm to fit Gaussian mixtures to Vertically Partitioned data (VP-EM). In federated learning setups, our algorithm matches the centralized EM fitting of Gaussian mixtures constrained to a subspace. In arbitrary communication graphs, consensus averaging allows VP-EM to run on large peer-to-peer networks as an EM approximation. This mismatch comes from consensus error only, which vanishes exponentially fast with the number of consensus rounds. We demonstrate VP-EM on various topologies for both synthetic and real data, evaluating its approximation of centralized EM and seeing that it outperforms the available benchmark.However, data is often too large, privacy sensitive, or both, preventing the use of a data center where conventional, centralized methods can be employed. Such setups call for distributed approaches where data is

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Section: Introductionmentioning

confidence: 87%

Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features

Valdeira¹,

Soares²

2022

Preprint

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“…Nonetheless, graph based clustering algorithms including those that produce “fuzzy clusters” (i.e., data points, genes or proteins that are included in more than one cluster) have not been in wide use in current biological research mainly because of prevailing traditions in which biologists favor an unambiguous and unique assignment for a gene or protein into a cluster. In this direction several recent publications make important algorithmic contributions (Altilio et al, 2019; Zhao & Sayed, 2015), and students of computational biology will do well to find their applications into biological problems. Increased future use of “fuzzy clusters” should therefore be encouraged, which might lead to novel and more realistic insights into postgenomic biology questions, for example is examining the community structures of microbial associations in the human gut for human health (Schmidt et al, 2018) or in the root‐soil system of plants for a better management of global greenhouse gases (Philippot et al, 2013).…”

Section: Unsupervised MLmentioning

confidence: 99%

Machine learning in postgenomic biology and personalized medicine

Ray

2022

WIREs Data Min & Knowl

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In recent years, machine learning (ML) has been revolutionizing biology, biomedical sciences, and gene‐based agricultural technology capabilities. Massive data generated in biological sciences by rapid and deep gene sequencing and protein or other molecular structure determination, on the one hand, require data analysis capabilities using ML that are distinctly different from classical statistical methods; on the other, these large datasets are enabling the adoption of novel data‐intensive ML algorithms for the solution of biological problems that until recently had relied on mechanistic model‐based approaches that are computationally expensive. This review provides a bird's eye view of the applications of ML in postgenomic biology. Attempt is also made to indicate as far as possible the areas of research that are poised to make further impacts in these areas, including the importance of explainable artificial intelligence in human health. Further contributions of ML are expected to transform medicine, public health, agricultural technology, as well as to provide invaluable gene‐based guidance for the management of complex environments in this age of global warming. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Prediction

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“…Distributed sequential convex programming methods have been applied to pose graph optimization problems [94] using a quadratic approximation of the objective function along with Gauss-Siedel updates to enable distributed local computations among the robots. The NEXT family of algorithms have been applied to a number of learning problems where data is distributed including semi-supervised support vector machines [95], neural network training [96], and clustering [97].…”

Section: Applications Of Distributed Sequential Convex Programmingmentioning

confidence: 99%

A Survey of Distributed Optimization Methods for Multi-Robot Systems

Halsted¹,

Shorinwa²,

Yu³

et al. 2021

Preprint

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Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and networkconstrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.

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Distributed data clustering over networks

Cited by 21 publications

References 36 publications

Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features

Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features

Machine learning in postgenomic biology and personalized medicine

A Survey of Distributed Optimization Methods for Multi-Robot Systems

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