Probabilistic subspace mixture models, as proposed over the last few years, are interesting methods for learning image manifolds, i.e. nonlinear subspaces of spaces in which images are represented as vectors by their grey-values. Their lack of a glo
Robust Statistics: Theory and Methods (Wiley Series in Probability and Statistics) (Hardcover) Preface. 1. Introduction. 1.1 Classical and robust approaches to statistics. 1.2 Mean and standard deviation. 1.3 The “three-sigma edit” rule. 1.4 Linear
Clues derived from the locations connected to violent repeat criminal offenders, such as serial murderers, rapists, and arsonists, can be of significant assistance to law enforcement. Such information allows police departments to focus their activit
In this dissertation we propose priors and learning based methods for super- resolution and other video processing applications. We also propose efficient al- gorithms for global motion estimation and projection on L1 ball under box con- straints. We
For the data contaminated by outliers,we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors,LRR can also approximately recover the
Wireless sensor networks (WSNs) have received considerable attention for multiple types of applications. In particular, outlier detection in WSNs has been an area of vast interest. Outlier detection becomes even more important for the applications i
In the ¯eld of wireless sensor networks, measurements that signi¯cantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the netwo
As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane. Rather, complications arise due to the fact that the object u
Practical data design tips from a data visualization expert of the modern age Data doesnt decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldnt it be wonderful if we could act
ABSTRACT Outliers are abnormal instances or observations. Detecting data outliers is a very important concept in Knowledge data discovery. Outlier detection has been studied in the context of a large number of research areas like large distributed s
