One line of SOCO's research concerns probabilistic unsupervised models for high dimensional data clustering and visualization. Specifically, we are interested in latent, non-linear manifold learning models for data visualization and clustering, and matrix factorization methods for blind source separation, with application to real-world problems in the areas of clinical medicine, ecology, and business processes, including collaborations with the Neural Computation Group at Liverpool John Moores University (UK) [go] and the GABRMN [go] and Systems Pharmacology and Bioinformatics [go] research groups at Universitat Autònoma de Barcelona (UAB).
Previous work of the group in this area has dealt with syntactic pattern recognition (mainly grammatical inference), structural pattern recognition (error-tolerant graph matching and probabilistic learning of graph models) and applications in computer vision (object recognition and face recognition). The problem of surface reconstruction from parallel cross sections, typical of 3D medical images, has also been studied, giving rise to a Ph.D. thesis dissertation on this subject. Current interests of SOCO in Pattern Recognition and Computer Vision include dynamic object recognition and tracking from image sequences and bio-inspired hierarchical neural architectures for visual processing.
The first five describe methodologies with a similar goal: the resolution of complex problems that cannot be efficiently solved by means of traditional computational methods (hard computing). Feature Selection and Extraction deals with problems of data dimensionality reduction that are present in all the previous lines. Finally, Pattern Recognition and Computer Vision copes with a huge application field where the performance of AI systems is usually worse than the human one, and where the soft computing techniques have great potential. Related to it, Data and Knowledge Visualization concerns ways in which Soft Computing complements human vision in problems of exploratory Data Mining.
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