Toward a Quantitative Survey of Dimension Reduction Techniques

By Mateus Espadoto, Rafael Messias Martins, Andreas Kerren, Nina Sumiko Tomita Hirata, Alexandru Cristian Telea. IEEE Transactions on Visualization and Computer Graphics, 2019

346 citations recorded by the source corpus.

Dimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4e7b11e07aaebe17de0ff3a720a6f9f6b70c7bc2

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In Artificial Intelligence

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Citations in the corpus

Deep learning multidimensional projections (2019, methodology): “Such metrics are often used in projection literature to compare different techniques that do not share implementation similarities.(3,34) Technique-specific metrics consider the cost functions used by specific techniques, and thus allow comparing these techniques to our method, that is, show how wel”