We present a correlation study of time-varying multivariate volumetric data sets. In most scientific disciplines, to test hypotheses and discover insights, scientists are interest...
The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learnin...
Current technology trends have led to the growing impact of both inter-die and intra-die process variations on circuit performance. While it is imperative to model parameter varia...
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common i...
Previous results for correlated block-fading MIMO channels with covariance information indicate guaranteed capacity growth with additional transmit elements and that in rapidly fa...