| Records |
Author  |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
| Title |
A flexible outlier detector based on a topology given by graph communities |
Type |
Miscellaneous |
| Year |
2020 |
Publication |
Arxiv |
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| Abstract |
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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no |
| Call Number |
Admin @ si @ RBG2020 |
Serial |
3475 |
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| Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
| Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
Type |
Journal Article |
| Year |
2022 |
Publication |
Big Data Research |
Abbreviated Journal |
BDR |
| Volume |
29 |
Issue |
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Pages |
100332 |
| Keywords |
Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
| Abstract |
Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. 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, like the number of neighbors.
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 and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
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August 28, 2022 |
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| Notes |
DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
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no |
| Call Number |
Admin @ si @ RBG2022a |
Serial |
3718 |
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| Author |
Oriol Ramos Terrades |
| Title |
Descripcio i classificacio de simbols tecnics usant la transformada de crestetes |
Type |
Report |
| Year |
2003 |
Publication |
CVC Technical Report #74 |
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CVC (UAB) |
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DAG |
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no |
| Call Number |
DAG @ dag @ Ram2003 |
Serial |
517 |
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| Author |
Oriol Ramos Terrades |
| Title |
Linear Combination of Multiresolution Descriptors: Application to Graphics Recognition |
Type |
Book Whole |
| Year |
2006 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC & Universite Nancy 2 |
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Ph.D. thesis |
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Editor |
Salvatore Antoine Tabbone;Ernest Valveny |
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DAG |
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no |
| Call Number |
DAG @ dag @ Ram2006 |
Serial |
713 |
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| Author |
Oriol Pujol; Sergio Escalera; Petia Radeva |
| Title |
An Incremental Node Embedding Technique for Error Correcting Output Codes |
Type |
Journal |
| Year |
2008 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
41 |
Issue |
2 |
Pages |
713–725 |
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| Notes |
MILAB;HuPBA |
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no |
| Call Number |
BCNPCL @ bcnpcl @ PER2008 |
Serial |
942 |
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| Author |
Oriol Pujol; Petia Radeva; Jordi Vitria; J. Mauri |
| Title |
Adaboost to Classify Plaque Appearance in IVUS Images |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
Progress in Pattern Recognition, Image Analysis and Applications, LNCS 3287:629–636 |
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Puebla (Mexico) |
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| Notes |
OR;MILAB;HuPBA;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PRV2004 |
Serial |
472 |
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| Author |
Oriol Pujol; Petia Radeva; Jordi Vitria |
| Title |
Traffic sign recognition using an adaptive boosting multiclass framework |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
Workshop sobre Reconocimiento de Formas y Analisis de Imagenes (AERFAI), I Congreso Español de Informatica (CEDI´2005) |
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Granada (Spain) |
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| Notes |
OR;MILAB;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ PRV2005 |
Serial |
618 |
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| Author |
Oriol Pujol; Petia Radeva; Jordi Vitria |
| Title |
Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes |
Type |
Journal |
| Year |
2006 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012 |
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| Notes |
OR;MILAB;HuPBA;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PRV2006a |
Serial |
646 |
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| Author |
Oriol Pujol; Petia Radeva; J. Mauri; E Fernandez-Nofrerias |
| Title |
Automatic segmentation of lumen in Intravascular Ultrasound Images: An evaluation of texture feature extractors. |
Type |
Miscellaneous |
| Year |
2002 |
Publication |
Iberoamerican Conference on Artificial Intelligence IBERAMIA 2002, Springer Verlag |
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Sevilla, Espanya |
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| Notes |
MILAB;HuPBA |
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no |
| Call Number |
BCNPCL @ bcnpcl @ PRM2002 |
Serial |
314 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Lumen Detection in Ivus Image Using Snakes in a Statical Framework. |
Type |
Miscellaneous |
| Year |
2002 |
Publication |
XX Congreso Anual de la Sociedad Española de Ingenieria Biomedica CASEIB 2002, 1: 129–132. |
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Saragossa, Espanya |
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| Notes |
MILAB;HuPBA |
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no |
| Call Number |
BCNPCL @ bcnpcl @ PuR2002 |
Serial |
315 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Texture Segmentation by Statistic Deformable Models |
Type |
Journal |
| Year |
2003 |
Publication |
International Journal of Image and Graphics (IJIG) |
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| Notes |
MILAB;HuPBA |
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no |
| Call Number |
BCNPCL @ bcnpcl @ PuR2003 |
Serial |
432 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Texture Segmentation by Statistical Deformable Models |
Type |
Journal |
| Year |
2004 |
Publication |
International Journal of Image and Graphics |
Abbreviated Journal |
IJIG |
| Volume |
4 |
Issue |
3 |
Pages |
433-452 |
| Keywords |
Texture segmentation, parametric active contours, statistic snakes |
| Abstract |
Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model. |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PuR2004a |
Serial |
505 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Solving Particularization with Supervised Clustering Competition Scheme |
Type |
Book Chapter |
| Year |
2005 |
Publication |
Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 11–18 |
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| Address |
Estoril (Portugal) |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PuR2005b |
Serial |
557 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
On the assessment of texture descriptors in intravascular ultrasound images: A boosting approach to a feasible plaque classification |
Type |
Book Chapter |
| Year |
2005 |
Publication |
Plaque Imaging: Pixel to Molecular Level, IOS Press, J. Suri et al. (Eds.), 113: 276–299, ISBN: 1–58603–516–9 |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PuR2005a |
Serial |
563 |
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| Author |
Oriol Pujol; Petia Radeva |
| Title |
Optimal extension of Error Correcting Output Codes |
Type |
Miscellaneous |
| Year |
2006 |
Publication |
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Perpignan (France) |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2006d |
Serial |
695 |
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