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Efficiently Learning Fourier Sparse Set Functions

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Transforms and Operators for Directional Bioimage Analysis: A Survey

Michaël Unser, Daniel Sage, Zsuzsanna Püspöki, Martin Kurt Storath

We give a methodology-oriented perspective on directional image analysis and rotation-invariant processing. We review the state of the art in the field and make connections with recent mathematical developments in functional analysis and wavelet theory. We ...

Springer International Publishing2016

Low-Rank Matrices on Graphs: Generalized Recovery & Applications

Pierre Vandergheynst, Nathanaël Perraudin, Nauman Shahid

Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space. Unfortunately, one often has very little or no information about the geometry of the space, resulting in a highly under-determined recovery problem. ...

2016

A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain

Martin Vetterli, Robin Scheibler, Saeid Haghighatshoar

In this paper, we design a new iterative low-complexity algorithm for computing the Walsh-Hadamard transform (WHT) of an N dimensional signal with a K-sparse WHT. We suppose that N is a power of two and K = O(N^α), scales sub-linearly in N for some α ∈ (0, ...

Institute of Electrical and Electronics Engineers2015

The Range of a Random Walk on a Comb

János Pach, Gábor Tardos

The graph obtained from the integer grid Z x Z by the removal of all horizontal edges that do not belong to the x-axis is called a comb. In a random walk on a graph, whenever a walker is at a vertex v, in the next step it will visit one of the neighbors of ...

Electronic Journal Of Combinatorics2013

Intelligent Clustering for Graph Visualization

Lionel Jérémie Martin

More and more areas use graphs for the representation of their data because it gives a connection-oriented perspective. Unfortunately, datasets are constantly growing in size, while devices have increasingly smaller screens (tablets, smartphones, etc). In ...

2013

Graph Matching for Adaptation in Remote Sensing

Devis Tuia

We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an a ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Sequences with Minimal Time-Frequency Spreads

Martin Vetterli, Reza Parhizkar, Yann Barbotin

For a given time or frequency spread, one can always find continuous- time signals, which achieve the Heisenberg uncertainty principle bound. This is known, however, not to be the case for discrete-time sequences; only widely spread sequences asymptoticall ...

Ieee2013

Graph Drawing

Radoslav Fulek, Fabrizio Frati

In this paper we study the page number of upward planar directed acyclic graphs. We prove that the page number of any upward planar directed acyclic graph G is a function of the page number of a four-connected subgraph of G; further, we provide an upper bo ...

Springer Berlin Heidelberg2012

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

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 ...

EPFL2011