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Chemiscope: interactive structure-property explorer for materials and molecules

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LSH At Large - Distributed KNN Search in High Dimensions

Karl Aberer, Sebastian Michel, Parisa Haghani

We consider K-Nearest Neighbor search for high dimensional data in large-scale structured Peer-to-Peer networks. We present an efficient mapping scheme based on p-stable Locality Sensitive Hashing to assign hash buckets to peers in a Chord-style overlay ne ...

2008

3D Face Recognition using Sparse Spherical Representations

Pascal Frossard, Effrosyni Kokiopoulou, Ivana Tosic, Roser Sala Llonch

This paper addresses the problem of 3D face recognition using spherical sparse representations. We first propose a fully automated registration process that permits to align the 3D face point clouds. These point clouds are then represented as signals on th ...

2008

Dimensionality Reduction with Adaptive Approximation

Pascal Frossard, Effrosyni Kokiopoulou

In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using redundant dictionaries. A redundant ...

2007

Orthogonal Neighborhood Preserving Projections: A projection-based dimensionality reduction technique

Effrosyni Kokiopoulou

This paper considers the problem of dimensionality reduction by orthogonal projection techniques. The main feature of the proposed techniques is that they attempt to preserve both the intrinsic neighborhood geometry of the data samples and the global geome ...

2007

Complex network analysis of free-energy landscapes

Paolo De Los Rios, David Gfeller

The kinetics of biomolecular isomerization processes, such as protein folding, is governed by a free-energy surface of high dimensionality and complexity. As an alternative to projections into one or two dimensions, the free-energy surface can be mapped in ...

National Academy of Sciences2007

A Taxonomy for Semi-Supervised Learning Methods

Matthias Seeger

We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. We give some broad classes of algorithms for each of the families and point to specific realizations in the literature. Finally, we shed more detailed ...

MIT Press2006

Méthodes infographiques pour l'apprentissage des agents autonomes virtuels

Toni Conde

There are two primary approaches to behavioural animation of an Autonomous Virtual Agent (AVA). The first one, or behavioural model, defines how AVA reacts to the current state of its environment. In the second one, or cognitive model, this AVA uses a thou ...

EPFL2005

Estimating the Intrinsic Dimension of Data with a Fractal-Based Method

Alessandro Vinciarelli

In this paper, the problem of estimating the Intrinsic Dimension of a data set is investigated. A fractal-based approach using the Grassberger-Procaccia algorithm is proposed. Since the Grassberger-Procaccia algorithm performs badly on sets of high dimensi ...

2002

Estimating the Intrinsic Dimension of Data with a Fractal-Based Method

Alessandro Vinciarelli

IDIAP2002

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000