Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Recent empirical work has shown that combining predictors can lead to significant reduction in generalization error. The individual predictors (weak learners) can be very simple, ...
We present the Conformal Embedding Analysis (CEA) for feature extraction and dimensionality reduction. Incorporating both conformal mapping and discriminating analysis, CEA projec...
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of the...
Le Song, Alex J. Smola, Karsten M. Borgwardt, Arth...
This paper proposes a method of finding a discriminative linear transformation that enhances the data's degree of conformance to the compactness hypothesis and its inverse. Th...