In this paper, we propose a novel method for projecting data from multiple modalities to new subspace optimized one-class classification. The proposed iteratively transforms the original feature space of each modality common along with finding joint compact description coming all modalities. For in modality, define separate transformation map corresponding by exploiting available information cl...

In this paper we investigate how the boundary of a data set can be obtained in case of (very) low sample sizes. This boundary can be used to detect if new objects resemble the data set and therefore make the subsequent classification more confident. When a large number of training objects is available it is possible to directly estimate the density. After thresholding the probability density a ...

Streaming algorithms aim to summarize a large volume of data into a compact summary, by maintaining a data structure that can be incrementally modified as updates are observed. They allow the approximation of particular quantities. The AMS Sketch is focused on approximating the sum of squared entries of a vector defined by a stream of updates. This quantity is naturally related to the Euclidean...