Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …

Integrating genetic perturbations with gene expression data not only improves accuracy of
regulatory network topology inference, but also enables learning of causal regulatory …

Principal component analysis (PCA) is widely used for dimensionality reduction, with well-
documented merits in various applications involving high-dimensional data, including …

Co** with outliers contaminating dynamical processes is of major importance in various
applications because mismatches from nominal models are not uncommon in practice. In …

The problem of source localization from time-difference-of-arrival (TDOA) measurements is
in general a non-convex and complex problem due to its hyperbolic nature. This problem …

Multidimensional scaling (MDS) seeks an embedding of N objects in ap<; N dimensional
space such that inter-vector distances approximate pairwise object dissimilarities. Despite …

Sparse representation has been extensively investigated for hyperspectral image (HSI)
classification and led to substantial improvements in the performance over the traditional …

This paper considers the task of recovering the support of a sparse, high-dimensional vector
from a small number of measurements. The procedure proposed here, which we call the …

Nonparametric methods are widely applicable to statistical inference problems, since they
rely on a few modeling assumptions. In this context, the fresh look advocated here …

Most real-world networks exhibit community structure, a phenomenon characterized by
existence of node clusters whose intra-edge connectivity is stronger than edge …