HLoOP - Hyperbolic 2-space Local Outlier Probabilities

Clémence Allietta (1) , Jean-Philippe Condomines (1) , Jean-Yves Tourneret (2, 3, 4) , Emmanuel Lochin (1)

1 ENAC - Ecole Nationale de l'Aviation Civile
2 IRIT - Institut de recherche en informatique de Toulouse
3 IRIT-MINDS - CoMputational imagINg anD viSion
4 Toulouse INP - Institut National Polytechnique (Toulouse)

Hyperbolic geometry has recently garnered considerable attention in machine learning due to its capacity to embed hierarchical graph structures with low distortions for further downstream processing. This paper introduces a simple framework to detect local outliers for datasets grounded in hyperbolic 2-space referred to as HLoOP (Hyperbolic Local Outlier Probability). Within a Euclidean space, well-known techniques for local outlier detection are based on the Local Outlier Factor (LOF) and its variant, the LoOP (Local Outlier Probability), which incorporates probabilistic concepts to model the outlier level of a data vector. The developed HLoOP combines the idea of finding nearest neighbors, density-based outlier scoring with a probabilistic, statistically oriented approach. Therefore, the method consists in computing the Riemmanian distance of a data point to its nearest neighbors following a Gaussian probability density function expressed in a hyperbolic space. This is achieved by defining a Gaussian cumulative distribution in this space. The HLoOP algorithm is tested on the WordNet dataset yielding promising results. Code and data will be made available on request for reproductibility.

Keywords: Outlier Detection; Hyperbolic Embedding

Domains: Engineering Sciences [physics]

Clémence Allietta, Jean-Philippe Condomines, Jean-Yves Tourneret, Emmanuel Lochin. HLoOP - Hyperbolic 2-space Local Outlier Probabilities. 2023. ⟨hal-04327289⟩