Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
Background: Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but ...
We present a surface compression method that stores surfaces as wavelet-compressed signed-distance volumes. Our approach enables the representation of surfaces with complex topolo...
Daniel E. Laney, Martin Bertram, Mark A. Duchainea...
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
We studied a number of measures that characterize the difficulty of a classification problem. We compared a set of real world problems to random combinations of points in this mea...