We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs....
Abstract— This paper proposes a maximum likelihood detection (MLD) method for the differential space-time block code (DSTBC) in cooperation with blind linear prediction (BLP) of ...
In this paper, we present an abstract framework for online approximation of time-series data that yields a unified set of algorithms for several popular models: data streams, amnes...
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
Approximate query processing has emerged as a costeffective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision-suppo...
Kaushik Chakrabarti, Minos N. Garofalakis, Rajeev ...