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Poster
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof Choromanski · Mark Rowland · Adrian Weller

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #54
in Posters Tue »

We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation. For both the Johnson-Lindenstrauss transform and the angular kernel, we show that we can select matrices yielding guaranteed improved performance in accuracy and/or speed compared to earlier methods. We introduce matrices with complex entries which give significant further accuracy improvement. We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

Keywords: Kernel Methods
[ Paper]  [ Poster] 

Author Information

Krzysztof Choromanski (Google Brain Robotics)
Mark Rowland (University of Cambridge)
Adrian Weller (University of Cambridge)

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

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