Publications associées à Scalable Low-rank Matrix and Tensor Decomposition on Graphs

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Principal Polynomial Analysis

Devis Tuia

This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of ma ...

World Scientific Publ Co Pte Ltd2014

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Majid Yazdani, Hassan Ghasemzadeh Mohammadi

With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling cha ...

2014

Dual compatible splines on nontensor product meshes

Annalisa Buffa, Rafael Vazquez Hernandez

In this paper we introduce the concept of dual compatible (DC) splines on nontensor product meshes, study the properties of this class, and discuss their possible use within the isogeometric framework. We show that DC splines are linear independent and tha ...

Springer, Cham2014

Tangent-based manifold approximation with locally linear models

Pascal Frossard, Sofia Karygianni

In this paper, we consider the problem of manifold approximation with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that represent manifold data accurately while preserving the manifold's structure. For this purpo ...

Elsevier2014

Two-Level Discretization Techniques For Ground State Computations Of Bose-Einstein Condensates

Patrick Henning

This work presents a new methodology for computing ground states of Bose-Einstein condensates based on finite element discretizations on two different scales of numerical resolution. In a preprocessing step, a low-dimensional (coarse) generalized finite el ...

Society for Industrial and Applied Mathematics2014

Low-rank tensor completion by Riemannian optimization

Daniel Kressner, Michael Maximilian Steinlechner

In tensor completion, the goal is to fill in missing entries of a partially known tensor under a low-rank constraint. We propose a new algorithm that performs Riemannian optimization techniques on the manifold of tensors of fixed multilinear rank. More spe ...

Springer2014

Exploring Local Modifications for Constrained Meshes

Mark Pauly, Sofien Bouaziz, Bailin Deng, Yuliy Gennadievich Schwartzburg, Mario Moacir Deuss

Mesh editing under constraints is a challenging task with numerous applications in geometric modeling, industrial design, and architectural form finding. Recent methods support constraint-based exploration of meshes with fixed connectivity, but commonly la ...

Wiley-Blackwell2013

Energy-aware adaptive bi-Lipschitz embeddings

Volkan Cevher, Bubacarr Bah, Ali Sadeghian

We propose a dimensionality reducing matrix design based on training data with constraints on its Frobenius norm and number of rows. Our design criteria is aimed at preserving the distances between the data points in the dimensionality reduced space as muc ...

2013

Scalable Low-rank Matrix and Tensor Decomposition on Graphs

Graph Chatbot

Chattez avec Graph Search

Principal Polynomial Analysis

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Dual compatible splines on nontensor product meshes

Tangent-based manifold approximation with locally linear models

Two-Level Discretization Techniques For Ground State Computations Of Bose-Einstein Condensates

Graph-based Image Inpainting

Low-rank tensor completion by Riemannian optimization

Exploring Local Modifications for Constrained Meshes

Counting Sheaves Using Spherical Codes

Energy-aware adaptive bi-Lipschitz embeddings

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Low-rank tensor completion by Riemannian optimization

Counting Sheaves Using Spherical Codes

Tangent-based manifold approximation with locally linear models

Dual compatible splines on nontensor product meshes

Energy-aware adaptive bi-Lipschitz embeddings

Graph-based Image Inpainting

Exploring Local Modifications for Constrained Meshes

Principal Polynomial Analysis

Two-Level Discretization Techniques For Ground State Computations Of Bose-Einstein Condensates