Publications related to Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Enhanced change detection using nonlinear feature extraction

This paper presents an application of the kernel principal component analysis aiming at aligning optical images before the application of change detection techniques. The approach relies on the extraction of nonlinear features from a selected subset of pix ...

2012

Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Dimitri Nestor Alice Van De Ville, Michel Kocher

Nonlocal means (NLM) is an effective denoising method that applies adaptive averaging based on similarity between neighborhoods in the image. An attractive way to both improve and speed-up NLM is by first performing a linear projection of the neighborhood. ...

IEEE2011

Learning to use novel objects: A training study on the acquisition of novel action representations

Michiel van Elk, Christian Pfeiffer

Many studies have suggested that the motor system is organized in a hierarchical fashion, around the prototypical end location associated with using objects. However, most studies supporting the hierarchical view have used well-known actions and objects th ...

2011

OctoMag: An Electromagnetic System for 5-DOF Wireless Micromanipulation

Ali Sengül

We demonstrate five-degree-of-freedom (5-DOF) wireless magnetic control of a fully untethered microrobot (3-DOF position and 2-DOF pointing orientation). The microrobot can move through a large workspace and is completely unrestrained in the rotation DOF. ...

2010

Geometry-aware analysis of high-dimensional visual information sets

Effrosyni Kokiopoulou

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobil ...

EPFL2009

Some Recovery Conditions for Basis Learning by L1-Minimization

Rémi Gribonval, Karin Schnass

Many recent works have shown that if a given signal admits a sufficiently sparse representation in a given dictionary, then this representation is recovered by several standard optimization algorithms, in particular the convex

\ell^1

minimization approac ...

2008

Tracking Articulated Bodies using Generalized Expectation Maximization

Pascal Fua, Andrea Fossati

A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analys ...

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

Analysis of Multimodal Signals Using Redundant Representations [Winner of IBM Student Paper Award]

Pierre Vandergheynst, Gianluca Monaci, Oscar Divorra Escoda

In this work we explore the potentialities of a framework for the representation of audio-visual signals using decompositions on overcomplete dictionaries. Redundant decompositions may describe audio-visual sequences in a concise fashion, preserving good r ...

2005

Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

Wulfram Gerstner

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in t ...

Institute of Electrical and Electronics Engineers2004

Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Graph Chatbot

Chat with Graph Search

Enhanced change detection using nonlinear feature extraction

Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Learning to use novel objects: A training study on the acquisition of novel action representations

OctoMag: An Electromagnetic System for 5-DOF Wireless Micromanipulation

Geometry-aware analysis of high-dimensional visual information sets

Some Recovery Conditions for Basis Learning by L1-Minimization

Tracking Articulated Bodies using Generalized Expectation Maximization

Dimensionality Reduction with Adaptive Approximation

Analysis of Multimodal Signals Using Redundant Representations [Winner of IBM Student Paper Award]

Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Enhanced change detection using nonlinear feature extraction

Learning to use novel objects: A training study on the acquisition of novel action representations

OctoMag: An Electromagnetic System for 5-DOF Wireless Micromanipulation

Geometry-aware analysis of high-dimensional visual information sets

Tracking Articulated Bodies using Generalized Expectation Maximization

Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

Some Recovery Conditions for Basis Learning by L1-Minimization

Dimensionality Reduction with Adaptive Approximation

Analysis of Multimodal Signals Using Redundant Representations [Winner of IBM Student Paper Award]