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Reconstructing complex curvilinear structures such as neural circuits, road networks, and blood vessels is a key challenge in many scientific and engineering fields. It has a broad range of applications, from the delineation of micrometer-sized neurons in ...
We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that th ...
We define the crossing number for an embedding of a graph G into R^3, and prove a lower bound on it which almost implies the classical crossing lemma. We also give sharp bounds on the space crossing numbers of pseudo-random graphs. ...
We live in a world characterized by massive information transfer and real-time communication. The demand for efficient yet low-complexity algorithms is widespread across different fields, including machine learning, signal processing and communications. Mo ...
Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...
In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...
Institute of Electrical and Electronics Engineers2012
Candidate layout patterns can be assessed using a sparse pattern dictionary of known design layout patterns by determining sparse coefficients for each candidate pattern, reconstructing the respective candidate pattern, and determining reconstruction error ...
U.S. Patent and Trademark Office; U.S. DEPARTMENT OF COMMERCE2013
Real-world data such as multimedia, biomedical, and telecommunication signals are formed of specific structures. However, these structures only determine some general properties of the data while the unknown or unpredictable parts are assumed to be random. ...
We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...
We design two different strategies for computing the unknown content preferences in an online social network based on a small set of nodes in the corresponding social graph for which this information is available ahead of time. The techniques take advantag ...
