We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled ...
S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri ...
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...
We propose a novel approach to shape-based image retrieval that builds upon a similarity criterion which is based on the average point set distance. Compared to traditional techni...
In this study, we propose two algorithms for measuring the distance between shape boundaries. In the algorithms, shape boundary is represented by the Beam Angle Statistics (BAS), w...
There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by explo...