We address covariance estimation under mean-squared loss in the Gaussian setting. Specifically, we consider shrinkage methods which are suitable for high dimensional problems wit...
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Linear techniques are widely used to reduce the dimension of image representation spaces in applications such as image indexing and object recognition. Optimal Component Analysis ...
Yiming Wu, Xiuwen Liu, Washington Mio, Kyle A. Gal...
Eficient indezing of high dimensional feature vectors is important to allow visual information systems and a number other applications to scale up to large databases. In this pape...
Abstract. In this paper, we consider whether traditional index structures are effective in processing unstable nearest neighbors workloads. It is known that under broad conditions...