Similarity Estimation Using Bayes Ensembles

Abstract

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 Lp-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. nearestneighbor classification and precision-recall-graphs.

Radiological images and machine learning: Trends, perspectives, and prospects

“…Recently, similarity or distance learning is a hot topic in the image retrieval field. Traditional choices include the Euclidean distance function, x 2 square distance function, Mahalanobis distance, l 1 norm distance function [38], maximum likelihood approach [200] and Bayes ensemble [201]. Like other machine learning tasks, features extraction is an important step in image retrieval systems.…”