Constant Time EXPected Similarity Estimation using Stochastic Optimization

Markus Schneider; Wolfgang Ertel; Günther Palm

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arXiv · 1511.05371

Constant Time EXPected Similarity Estimation using Stochastic Optimization

Markus Schneider·Wolfgang Ertel·Günther Palm

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Abstract

A new algorithm named EXPected Similarity Estimation (EXPoSE) was recently proposed to solve the problem of large-scale anomaly detection. It is a non-parametric and distribution free kernel method based on the Hilbert space embedding of probability measures. Given a dataset of $n$ samples, EXPoSE needs only $\mathcal{O}(n)$ (linear time) to build a model and $\mathcal{O}(1)$ (constant time) to make a prediction. In this work we improve the linear computational complexity and show that an $ε$-accurate model can be estimated in constant time, which has significant implications for large-scale learning problems. To achieve this goal, we cast the original EXPoSE formulation into a stochastic optimization problem. It is crucial that this approach allows us to determine the number of iteration based on a desired accuracy $ε$, independent of the dataset size $n$. We will show that the proposed stochastic gradient descent algorithm works in general (possible infinite-dimensional) Hilbert spaces, is easy to implement and requires no additional step-size parameters.

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BibTeX RIS

Markus Schneider, Wolfgang Ertel, Günther Palm. 2015-11-17. Constant Time EXPected Similarity Estimation using Stochastic Optimization. https://arxiv.org/abs/1511.05371

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