Concept

Curse of dimensionality

Related publications (331)

Randomized Projections for Improved Sensing and Imaging in Positron Emission Tomography

Positron Emission Tomography (PET) aims at recovering the metabolic activity of an organ of interest. Established algorithms implemented in contemporary PET scans are based on an approximation of the inverse Radon transform, resulting in a suboptimal estim ...

2017

Robust Discriminative Clustering with Sparse Regularizers

Nicolas Henri Bernard Flammarion

Clustering high-dimensional data often requires some form of dimensionality reduction, where clustered variables are separated from "noise-looking" variables. We cast this problem as finding a low-dimensional projection of the data which is well-clustered. ...

2017

Compressive Embedding and Visualization using Graphs

Pierre Vandergheynst, Nathanaël Perraudin, Johann Paratte

Visualizing high-dimensional data has been a focus in data analysis communities for decades, which has led to the design of many algorithms, some of which are now considered references (such as t-SNE for example). In our era of overwhelming data volumes, t ...

arXiv e-print archive2017

Miniature hyperspectral systems

Dragos Constantin

Spectroscopy methods have been used for decades to obtain information about various materials, ranging from galaxies billions of light years from earth, to Petri dishes containing rich bacteria cultures. From spectrometers in chemical labs to spectral came ...

EPFL2017

Introduction to the IEEE Journal on Selected Topics in Signal Processing and IEEE Transactions on Signal and Information Processing Over Networks Joint Special Issue on Graph Signal Processing

Pascal Frossard, Pier Luigi Dragotti

The papers in this special issue are intended to address some of the main research challenges in Graph Signal Processing by presenting a collection of the latest advances in the domain. These papers examine key representation, learning and processing aspec ...

2017

Universal adversarial perturbations

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for co ...

Ieee2017

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Michael Maximilian Steinlechner

In this thesis, we present a Riemannian framework for the solution of high-dimensional optimization problems with an underlying low-rank tensor structure. Here, the high-dimensionality refers to the size of the search space, while the cost function is scal ...

EPFL2016

The brain strategy for online learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying

Complexity is a double-edged sword for learning algorithms when the number of available samples for training in relation to the dimension of the feature space is small. This is because simple models do not sufficiently capture the nuances of the data set, ...

IEEE2016

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

Daniel Kressner, André Uschmajew

Low-rank tensor approximation techniques attempt to mitigate the overwhelming complexity of linear algebra tasks arising from high-dimensional applications. In this work, we study the low-rank approximability of solutions to linear systems and eigenvalue p ...

Elsevier Science Inc2016

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

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