Abstract
Similarity search and data mining often rely on distance or similarity functions in order to provide meaningful results and semantically meaningful patterns. However, standard distance measures like L p -norms are often not capable to accurately mirror the expected similarity between two objects. To bridge the so-called semantic gap between feature representation and object similarity, the distance function has to be adjusted to the current application context or user. In this paper, we propose a new probabilistic framework for estimating a similarity value based on a Bayesian setting. In our framework, distance comparisons are modeled based on distribution functions on the difference vectors. To combine these functions, a similarity score is computed by an Ensemble of weak Bayesian learners for each dimension in the feature space. To find independent dimensions of maximum meaning, we apply a space transformation based on eigenvalue decomposition. In our experiments, we demonstrate that our new method shows promising results compared to related Mahalanobis learners on several test data sets w.r.t. nearest-neighbor classification and precision-recall-graphs.
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Emrich, T., Graf, F., Kriegel, HP., Schubert, M., Thoma, M. (2010). Similarity Estimation Using Bayes Ensembles. In: Gertz, M., Ludäscher, B. (eds) Scientific and Statistical Database Management. SSDBM 2010. Lecture Notes in Computer Science, vol 6187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13818-8_37
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DOI: https://doi.org/10.1007/978-3-642-13818-8_37
Publisher Name: Springer, Berlin, Heidelberg
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