Data-Driven Computational Intelligence for Scientific Programming

Rubio-Largo, Á., Preciado, J. C., & Iribarne, L. (2019). Data-Driven Computational Intelligence for Scientific Programming. Scientific Programming,[5235706].[Editorial]. Doi: https://doi.org/10.1155/2019/5235706publishersversionpublishe

Discriminative dimensionality reduction: variations, applications, interpretations

Schulz A. Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld; 2017.The amount of digital data increases rapidly as a result of advances in information and sensor technology. Because the data sets grow with respect to their size, complexity and dimensionality, they are no longer easily accessible to a human user. The framework of dimensionality reduction addresses this problem by aiming to visualize complex data sets in two dimensions while preserving the relevant structure. While these methods can provide significant insights, the problem formulation of structure preservation is ill-posed in general and can lead to undesired effects. In this thesis, the concept of discriminative dimensionality reduction is investigated as a particular promising way to indicate relevant structure by specifying auxiliary data. The goal is to overcome challenges in data inspection and to investigate in how far discriminative dimensionality reduction methods can yield an improvement. The main scientific contributions are the following: (I) The most popular techniques for discriminative dimensionality reduction are based on the Fisher metric. However, they are restricted in their applicability as concerns complex settings: They can only be employed for fixed data sets, i.e. new data cannot be included in an existing embedding. Only data provided in vectorial representation can be processed. And they are designed for discrete-valued auxiliary data and cannot be applied to real-valued ones. We propose solutions to overcome these challenges. (II) Besides the problem that complex data are not accessible to humans, the same holds for trained machine learning models which often constitute black box models. In order to provide an intuitive interface to such models, we propose a general framework which allows to visualize high-dimensional functions, such as regression or classification functions, in two dimensions. (III) Although nonlinear dimensionality reduction techniques illustrate the structure of the data very well, they suffer from the fact that there is no explicit relationship between the original features and the obtained projection. We propose a methodology to create a connection, thus allowing to understand the importance of the features. (IV) Although linear mappings constitute a very popular tool, a direct interpretation of their weights as feature relevance can be misleading. We propose a methodology which enables a valid interpretation by providing relevance bounds for each feature. (V) The problem of transfer learning without given correspondence information between the source and target space and without labels is particularly challenging. Here, we utilize the structure preserving property of dimensionality reduction methods to transfer knowledge in a latent space given by dimensionality reduction

Dissimilarity-based learning for complex data

Mokbel B. Dissimilarity-based learning for complex data. Bielefeld: Universität Bielefeld; 2016.Rapid advances of information technology have entailed an ever increasing amount of digital data, which raises the demand for powerful data mining and machine learning tools. Due to modern methods for gathering, preprocessing, and storing information, the collected data become more and more complex: a simple vectorial representation, and comparison in terms of the Euclidean distance is often no longer appropriate to capture relevant aspects in the data. Instead, problem-adapted similarity or dissimilarity measures refer directly to the given encoding scheme, allowing to treat information constituents in a relational manner. This thesis addresses several challenges of complex data sets and their representation in the context of machine learning. The goal is to investigate possible remedies, and propose corresponding improvements of established methods, accompanied by examples from various application domains. The main scientific contributions are the following: (I) Many well-established machine learning techniques are restricted to vectorial input data only. Therefore, we propose the extension of two popular prototype-based clustering and classification algorithms to non-negative symmetric dissimilarity matrices. (II) Some dissimilarity measures incorporate a fine-grained parameterization, which allows to configure the comparison scheme with respect to the given data and the problem at hand. However, finding adequate parameters can be hard or even impossible for human users, due to the intricate effects of parameter changes and the lack of detailed prior knowledge. Therefore, we propose to integrate a metric learning scheme into a dissimilarity-based classifier, which can automatically adapt the parameters of a sequence alignment measure according to the given classification task. (III) A valuable instrument to make complex data sets accessible are dimensionality reduction techniques, which can provide an approximate low-dimensional embedding of the given data set, and, as a special case, a planar map to visualize the data's neighborhood structure. To assess the reliability of such an embedding, we propose the extension of a well-known quality measure to enable a fine-grained, tractable quantitative analysis, which can be integrated into a visualization. This tool can also help to compare different dissimilarity measures (and parameter settings), if ground truth is not available. (IV) All techniques are demonstrated on real-world examples from a variety of application domains, including bioinformatics, motion capturing, music, and education