Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin

On the optimal decision rule for sequential interactive structured prediction

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters [Volume 33, Issue 16, 1 December 2012, Pages 2226–2231] DOI: 10.1016/j.patrec.2012.07.010[EN] Interactive structured prediction (ISP) is an emerging framework for structured prediction (SP) where the user and the system collaborate to produce a high quality output. Typically, search algorithms applied to ISP problems have been based on the algorithms for fully-automatic SP systems. However, the decision rule applied should not be considered as optimal since the goal in ISP is to reduce human effort instead of output errors. In this work, we present some insight into the theory of the sequential ISP search problem. First, it is formulated as a decision theory problem from which a general analytical formulation of the opti- mal decision rule is derived. Then, it is compared with the standard formulation to establish under what conditions the standard algorithm should perform similarly to the optimal decision rule. Finally, a general and practical implementation is given and evaluated against three classical ISP problems: interactive machine translation, interactive handwritten text recognition, and interactive speech recognition.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 287576 (CasMaCat), and from the Spanish MEC/MICINN under the MIPRCV "Consolider Ingenio 2010" program (CSD2007-00018) and iTrans2 (TIN2009-14511) project. It is also supported by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/01) and GV/2010/067. The authors thank the anonymous reviewers for their criticisms and suggestions.Alabau, V.; Sanchis Navarro, JA.; Casacuberta Nolla, F. (2012). On the optimal decision rule for sequential interactive structured prediction. Pattern Recognition Letters. 33(16):2226-2231. https://doi.org/10.1016/j.patrec.2012.07.010S22262231331

Development of Transformations from Business Process Models to Implementations by Reuse

This paper presents an approach for developing transformations from business process models to implementations that facilitates reuse. A transformation is developed as a composition of three smaller tasks: pattern recognition, pattern realization and activity transformation. The approach allows one to reuse the definition and implementation of pattern recognition and pattern realization in the development of transformations targeting different business process modeling and implementation languages. In order to decouple pattern recognition and pattern realization, the approach includes a pattern language to represent the output of the pattern recognition task, which forms the input of the pattern realization task