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In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are(More)
—We propose a novel algorithm for automatic text line extraction on color and grayscale manuscript pages without prior binarization. Our algorithm is based on seam carving to compute separating seams between text lines. Seam carving is likely to produce seams that move through gaps between neighboring lines, if no information about the text geometry is(More)
Error-Correcting Output Codes (ECOC) with sub-classes reveal a common way to solve multi-class classification problems. According to this approach, a multi-class problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast(More)
Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique , a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multi-class(More)
— Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we(More)
We investigate the influence of low-level image features for aesthetics prediction. We show that the aesthetic quality of a photography depends on its context. Image features learned from a specific image category are not necessarily the same as features learned from a generic image collection. Experiments conducted on specific image categories show that(More)
This paper introduces a novel approach to data analysis in social sciences designed for the needs of specialists in the psychology of religion. We detect developmental and cross-cultural patterns in children's drawings of God(s) and other supernatural agents. The popular methods in social sciences lack objectivity and are heavily influenced by the(More)
—In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which capture the discriminant information that lies in the subspace orthogonal to the standard Laplacian SVMs.(More)
Error-Correcting Output Codes (ECOCs) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclasses technique (sub-ECOC), where by splitting the initial classes of the problem we aim(More)
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