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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.17 n.4 México Oct./Dec. 2013

 

Resumen de tesis

 

Generative Manifold Learning for the Exploration of Partially Labeled Data

 

Aprendizaje generativo de variedades para la exploración de datos parcialmente etiquetados

 

Raúl Cruz-Barbosa1, Alfredo Vellido2

 

1 Instituto de Computación, Universidad Tecnológica de la Mixteca, Huajuapan, Oaxaca, México. rcruz@mixteco.utm.mx

2 Departament de Llenguatges i Sistemes Informatics, Universitat Politecnica de Catalunya, Barcelona, Spain. avellido@lsi.upc.edu

 

Article received on 23/12/2011
Accepted on 18/06/2013

 

Abstract

In many real-world application problems, the availability of data labels for supervised learning is rather limited and incompletely labeled datasets are commonplace in some of the currently most active areas of research. A manifold learning model, namely Generative Topographic Mapping (GTM), is the basis of the methods developed in the thesis reported in this paper. A variant of GTM that uses a graph approximation to the geodesic metric is first defined. This model is capable of representing data of convoluted geometries. The standard GTM is here modified to prioritize neighbourhood relationships along the generated manifold. This is accomplished by penalizing the possible divergences between the Euclidean distances from the data points to the model prototypes and the corresponding geodesic distances along the manifold. The resulting Geodesic GTM (Geo-GTM) model is shown to improve the continuity and trustworthiness of the representation generated by the model, as well as to behave robustly in the presence of noise. We then proceed to define a novel semi-supervised model, SS-Geo-GTM, that extends Geo-GTM to deal with semi-supervised problems. In SS-Geo-GTM, the model prototypes obtained from Geo-GTM are linked by the nearest neighbour to the data manifold. The resulting proximity graph is used as the basis for a class label propagation algorithm. The performance of SS-Geo-GTM is experimentally assessed via accuracy and Matthews correlation coefficient, comparing positively with an Euclidean distance-based counterpart and the alternative Laplacian Eigenmaps and semi-supervised Gaussian mixture models.

Keywords: Semi-supervised learning, Clustering, Generative Topographic Mapping, Exploratory Data Analysis.

 

Resumen

En muchos problemas aplicados del mundo real, la disponibilidad de etiquetas de los datos para el aprendizaje supervisado es bastante limitada y los conjuntos de datos etiquetados incompletamente son habituales en algunas de las áreas de investigación actualmente mas activas. Un modelo de aprendizaje de variedades, el Mapeo Topográfico Generativo (GTM como acrónimo del nombre en inglés), es la base de los métodos desarrollados en la tesis reportada en este artículo. Se define en primer lugar una extensión de GTM que utiliza una aproximacion de grafos para la métrica geodésica. Este modelo es capaz de representar datos de geometría intrincada. El GTM estándar se modifica aquí para priorizar relaciones de vecindad a lo largo de la variedad generada. Esto se logra penalizando las divergencias posibles entre las distancias euclideanas de los puntos de datos a los prototipos del modelo y las distancias geodésicas correspondientes a lo largo de la variedad. Se muestra aquí que el modelo GTM geodésico (Geo-GTM) resultante mejora la continuidad y la fiabilidad de la representacion generada por el modelo, al igual que se comporta robustamente en presencia de ruido. Después, procedemos a definir un modelo semi-supervisado novedoso, SS-Geo-GTM, que extiende Geo-GTM para tratar problemas semi-supervisados. En SS-Geo-GTM, los prototipos del modelo obtenidos de Geo-GTM son vinculados mediante el vecino mas cercano a la variedad de datos. El grafo de proximidad resultante se utiliza como la base para un algoritmo de propagación de etiquetas de clase. El rendimiento de SS-Geo-GTM se evalúa experimentalmente a travos de las medidas de exactitud y el coeficiente de correlación de Matthews, comparando positivamente con una contraparte basada en la distancia euclideana y con los modelos alternativos de Eigenmapas Laplacianos y mezclas de Gaussianas semi-supervisadas.

Palabras clave: Aprendizaje semi-supervisado, agrupamiento, mapeo topográfico generativo, análisis exploratorio de datos.

 

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Acknowledgements

R. Cruz-Barbosa acknowledges the Mexican Secretariat of Public Education (SEP-PROMEP program) for his PhD grant.

 

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