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Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification

Related publications (37)

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

Jean-Philippe Thiran, Devis Tuia, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the p ...

2014

Matrix Completion on Graphs

Pierre Vandergheynst, Michael Bronstein, Xavier Bresson, Vasilis Kalofolias

The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Al- though the problem under the standard low rank assumption is NP-hard, Cande`s and Recht sho ...

2014

Unsupervised change detection with kernels

Devis Tuia

In this letter, an unsupervised kernel-based approach to change detection is introduced. Nonlinear clustering is utilized to partition in two a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained ...

Ieee-Inst Electrical Electronics Engineers Inc2012

Remote sensing image segmentation by active queries

Devis Tuia

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained ...

2012

Data mining methodologies for supporting engineers during system identification

Sandro Saitta

Data alone are worth almost nothing. While data collection is increasing exponentially worldwide, a clear distinction between retrieving data and obtaining knowledge has to be made. Data are retrieved while measuring phenomena or gathering facts. Knowledge ...

EPFL2008

Clustering protein environments for function prediction: finding PROSITE motifs in 3D

Giovanni De Micheli

Background: Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previous ...

BioMed Central2007

Prior Knowledge in Kernel Methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

École Polytechnique Fédérale de Lausanne2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Automatic genre classification of music content

Giorgio Zoia, Nicolas Scaringella

This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with ...

2006

Robust audio segmentation

Jitendra Ajmera

Audio segmentation, in general, is the task of segmenting a continuous audio stream in terms of acoustically homogenous regions, where the rule of homogeneity depends on the task. This thesis aims at developing and investigating efficient, robust and unsup ...

EPFL2005