Dimensionality-Reduction Algorithms for Progressive Visual Analytics

In this thesis, we present novel algorithmic solutions that enable integration of non-linear dimensionality-reduction techniques in visual analytics systems. Our proposed algorithms are, not only much faster than existing solutions, but provide richer insights into the data at hand. This result, is achieved by introducing new data processing and optimization techniques and by embracing the recently introduced concept of Progressive Visual Analytics; a computational paradigm that enables the interactivity of complex analytics techniques by means of visualization as well as interaction with intermediate results. Moreover, we present several applications that are designed to provide unprecedented analytical capabilities in several domains. These applications are powered by the algorithms introduced in this dissertation and led to several discoveries in areas ranging from the biomedical research field, to social-network data analysis and machine-learning models interpretability.