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Non-linear Low-rank and Sparse Representation for Hyperspectral Image Analysis

Related publications (36)

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Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

Towards large scale multimedia indexing: A case study on person discovery in broadcast news

Jean-Marc Odobez

The rapid growth of multimedia databases and the human interest in their peers make indices representing the location and identity of people in audio-visual documents essential for searching archives. Person discovery in the absence of prior identity knowl ...

2017

Graph learning under sparsity priors

Pascal Frossard, Hermina Petric Maretic

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Ieee2017

Graph-based structures in data science

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show l ...

EDM2016

Assembling a Network out of Ambiguous Patches

Matthias Grossglauser, Lyudmila Yartseva, Jefferson Elbert Simões

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small {\em patches} or subgraphs, of the true net ...

2016

Signal structure: from manifolds to molecules and structured sparsity

Sofia Karygianni

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent struct ...

EPFL2015

Exploring information retrieval using image sparse representations

William Guicquéro

New advances in the field of image sensors (especially in CMOS technology) tend to question the conventional methods used to acquire the image. Compressive Sensing (CS) plays a major role in this, especially to unclog the Analog to Digital Converters which ...

EPFL2015

Hyperspectral remote sensing of crop properties with Unmanned Aerial Vehicles

Martin Rehak, Yosef Akhtman, Dragos Constantin

We present a case study conducted over the Field Phenotyping Platform (FIP) at the ETH Zürich research station for plant sciences at Eschikon, Lindau, as collaboration between EPFL TOPO laboratory, Gamaya company and the ETHZ Crop Science Laboratory. The a ...

2015