Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projecti...
We present a reduction method that reduces a graph to a smaller core graph which behaves invariant with respect to planarity measures like crossing number, skewness, and thickness....
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
We introduce a master–worker framework for parallel global optimization of computationally expensive functions using response surface models. In particular, we parallelize two r...
An improved maximum likelihood estimator for ellipse fitting based on the heteroscedastic errors-in-variables (HEIV) regression algorithm is proposed. The technique significantly ...