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In this paper we develop and evaluate two methods for relevance feedbackbased on endowing a suitable "semantic query space" with a Riemann metricderived from the probability distribution of the positive samples of thefeedback. The first method uses a Gaussian distribution to model the data,while the second uses a more complex Latent Semantic variable model. A mixed(discrete-continuous) version of the Expectation-Maximization algorithm isdeveloped for this model.  We motivate the need for the semantic query space by analyzing in some depththree well-known relevance feedback methods, and we develop a new experimentalmethodology to evaluate these methods and compare their performance in aneutral way, that is, without making assumptions on the system in which theywill be embedded.

Relevance Feedback with Latent Variables in Riemann spaces