In this paper we show that dimensionality reduction (i.e., Johnson-Lindenstrauss lemma) preserves not only the distances between static points, but also between moving points, and...
We describe a way of using multiple different types of similarity relationship to learn a low-dimensional embedding of a dataset. Our method chooses different, possibly overlappin...
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
When assessing reported classification results based on selection of members from a database (e.g. a face database), one would like to know what is an achievable classification ra...
In this paper a new approach for classification is presented, the "Fast Projection Plane Classfier", abbreviated here to FPPC. The main idea of FPPC is very simple: A cl...