An in-depth review of SOMs with applications

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dc.contributor.advisor García Portugues, Eduardo
dc.contributor.author Jiménez Rama, Óscar Manuel
dc.date.accessioned 2020-05-28T10:50:59Z
dc.date.available 2020-05-28T10:50:59Z
dc.date.issued 2019-09
dc.date.submitted 2019-10-16
dc.identifier.uri http://hdl.handle.net/10016/30530
dc.description.abstract The development of internet has opened a new gate from where data can be collected. It is expected that in 2020, each human being will create 1,7 MB of information per second. The management of this huge quantity of data brings new problems and limitations. New analysis techniques are needed to overcome such obstacles to ultimately extract actionable insights from high volumes of data. The adequate analysis of this massive information, not only can be used in the business world to solve problems and take optimal decisions, it also pushes forward research and innovation in many fields of study. From a statistical point of view, more information not always leads to better predictions: when data “grows”, this usually implies the analysis of higher dimensional spaces in which more complex data structures may be present. Dimensionality reduction techniques, such as Principal Components Analysis (PCA), were conceived to give solution to this problem. Many of these techniques are of a linear nature and, as a consequence, hold limitations when applied to complex data structures. For this reason, many non linear procedures have been proposed in the last decades. Self-Organizing Maps (SOMs) have been gaining popularity over the years with the rise of computing power and since they bring a distinctive approach for high dimensional data inspection focusing on data clustering. SOMs ability to map high dimensional data into a low-dimensional space (commonly two-dimensional) is motivated by certain evidences found in cerebral nature. More specifically, with the studies regarding auto associative memory and its relation with neurons ability to learn. In this Bachelor Thesis, a complete review of SOMs is carried out. First, theoretical foundations are addressed, contextualizing the technique in its corresponding category within machine learning. Second, an in-depth description from a mathematical point of view is presented with a pedagogic aim. Third, software implementation is discussed using practical examples applied to synthetic data, in order to illustrate how the algorithm works in practice. Finally, an application of SOMs, related with the performance of football players, is presented in order to exploit its properties and extract insights from data. The codes of the examples can be openly accessed from the following GitHub repository: https://github.com/Oscarm96/SOMexamples
dc.language.iso eng
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Self-Organizing Maps
dc.subject.other Data Science
dc.subject.other Unsupervised Learning
dc.subject.other Dimensionality Reduction
dc.title An in-depth review of SOMs with applications
dc.type bachelorThesis
dc.subject.eciencia Estadística
dc.subject.eciencia Telecomunicaciones
dc.rights.accessRights openAccess
dc.description.degree Ingeniería en Tecnologías de Telecomunicación
dc.contributor.departamento Universidad Carlos III de Madrid. Departamento de Estadística
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