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Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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A flexible outlier detector based on a topology given by graph communities |
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2020 |
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Arxiv |
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Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. |
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IAM; DAG; 600.139; 600.145; 600.140; 600.121 |
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Admin @ si @ RBG2020 |
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3475 |
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Author |
Debora Gil |
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Title |
Regularized Curvature Flow |
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Report |
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2002 |
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CVC Technical Report |
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63 |
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Computer Vision Centre |
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IAM; |
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IAM @ iam @ Gil2002 |
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1518 |
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Author |
Debora Gil; Petia Radeva |
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Curvature based Distance Maps |
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2003 |
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CVC Technical Report |
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70 |
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Computer Vision Center |
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IAM;MILAB |
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IAM @ iam @ GIR2003a |
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1534 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
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Title |
A Deterministic-Statistical Strategy for Adventitia Segmentation in IVUS images |
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2005 |
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CVC Technical Report |
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89 |
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A useful tool for some specific studies in cardiac disease diagnosis is vessel plaque assessment by analysis of IVUS sequences. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts and blurred signal response due to ultrasound physical properties troubles automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of inter-observer variability regardless of plaque nature, vessel geometry and incomplete vessel borders. |
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IAM; MILAB |
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IAM @ iam @ HGR2005a |
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1548 |
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