Semi-Supervised Self-Organizing Maps with Time-Varying Structures for Clustering and Classification

• Autor(a) principal: BRAGA, Pedro Henrique Magalhães
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional da UFPE
• dARK ID: ark:/64986/001300000nmdc
• Texto Completo: https://repositorio.ufpe.br/handle/123456789/33484
• topic: Inteligência Computacional; Mapas Auto-Organizáveis; Aprendizagem Semi-Supervisionada

In recent years, the advances in technology have produced datasets of increasing size, not only regarding the number of samples but also the number of features. Unfortunately, despite these advances, creating a sufficiently large amount of properly labeled data with enough examples for each class is not an easy task. Organizing and labeling such data is challenging, expensive, and time-consuming. Also, it is usually done manually, and people can label with different formats and styles, incorporating noise and errors to the dataset. Hence, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Therefore, at the current stage of research, it is of great importance to put forward semi-supervised learning models aiming to combine both types of data, in order to benefit from the distinct information they can provide, to obtain better performances of both clustering and classification tasks, that would expand the range of machine learning applications. Moreover, it is also important to develop methods that are easy to parameterize in a way that become robust to the different characteristics of the data at hand. In this sense, the Self-Organizing Maps (SOM) can be considered as good options to address such objectives. It is a biologically inspired neural model that uses unsupervised and incremental learning to produce prototypes of the input data. However, such an unsupervised characteristic makes it unfeasible for SOM to execute Semi-Supervised Learning. In that way, this Dissertation presents some new proposals based on SOM to perform Semi-Supervised learning tasks for both clustering and classification. It is done by introducing to SOM the standard concepts of Learning Vector Quantization (LVQ), which can be seen as its supervised counterpart, to build hybrid approaches. Such proposals can dynamically switch between the two types of learning at training time, according to the availability of labels and automatically adjust themselves to the local variance observed in each data cluster. In the course of this work, the experimental results show that the proposed models can surpass the performance of other traditional methods not only in terms of classification but also regarding clustering quality. It also enhances the range of possible applications of a SOM and LVQ-based models by combining them with recent and promising techniques from Deep Learning to solve more complex problems commonly found in such field.