In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...
We introduce dynamic correlated topic models (DCTM) for analyzing discrete data over time. This model is inspired by the hierarchical Gaussian process latent variable models (GP-L...
In this paper we introduce a new M-tree building method, utilizing the classic idea of forced reinsertions. In case a leaf is about to split, some distant objects are removed from...
There are several pieces of information that can be utilized in order to improve the efficiency of similarity searches on high-dimensional data. The most commonly used information...
This paper explores refinements to methods used in a procedure being developed by the authors to personalize user interfaces for online shopping support tools. In the authors’ ...
Timothy Maciag, Daryl H. Hepting, Robert J. Hilder...