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Distance geometry and data science

Leo Liberti(liberti***at***lix.polytechnique.fr)

Abstract: Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in vectorial form. In this survey we discuss the fundamental problem of mapping graphs to vectors, and its relation with mathematical programming. We discuss applications, solution methods, dimensional reduction techniques and some of their limits. We then present an application of some of these ideas to neural networks, showing that distance geometry techniques can give competitive performance with respect to more traditional graph-to-vector mappings.

Keywords: Euclidean distance, Isometric embedding, Random projection, Mathematical Programming, Machine Learning, Artificial Neural Networks

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Combinatorial Optimization (Graphs and Matroids )

Category 3: Global Optimization (Applications )

Citation: To appear on TOP as an invited survey in 2020, Issue 2.

Download: [PDF]

Entry Submitted: 09/17/2019
Entry Accepted: 09/17/2019
Entry Last Modified: 09/17/2019

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