Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan...
In this paper, we present a mixture Principal Component Analysis (mPCA)-based approach for voxel level quantification of dynamic positron emission tomography (PET) data in brain s...
In this paper we introduce a novel Riemannian framework for shape analysis of parameterized surfaces. We derive a distance function between any two surfaces that is invariant to r...
Sebastian Kurtek, Eric Klassen, Anuj Srivastava, Z...
This paper presents novel methodologies for the analysis of continuous cellular tower data from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of...
In this paper, we present a novel video search interface based on the concept of aspect browsing. The proposed strategy is to assist the user in exploratory video search by active...
Thierry Urruty, Frank Hopfgartner, David Hannah, D...