Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of co...
The use of a wrist-worn sensor that is able to read nearby RFID tags and the wearer's gestures has been suggested frequently as a way to both detect the objects we interact w...
Eugen Berlin, Jun Liu, Kristof Van Laerhoven, Bern...
: The rapid progresses in human genome project and biotechnologies result in the sheer volume of datasets associated with in-depth scientific knowledge. Metabolomics is defined as ...
Algorithms are developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place ...