Sciweavers

Share
62 search results - page 1 / 13
» Non-negative Sparse Modeling of Textures
Sort
View
JMLR
2010
195views more  JMLR 2010»
9 years 8 months ago
Online Learning for Matrix Factorization and Sparse Coding
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
ICML
2005
IEEE
10 years 11 months ago
Non-negative tensor factorization with applications to statistics and computer vision
We derive algorithms for finding a nonnegative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = ...
Amnon Shashua, Tamir Hazan
JMLR
2008
188views more  JMLR 2008»
9 years 10 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
SCALESPACE
2007
Springer
10 years 4 months ago
Non-negative Sparse Modeling of Textures
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...
Gabriel Peyré
ICPR
2010
IEEE
10 years 4 months ago
Sparse Coding of Linear Dynamical Systems with an Application to Dynamic Texture Recognition
Given a sequence of observable features of a linear dynamical system (LDS), we propose the problem of finding a representation of the LDS which is sparse in terms of a given dict...
Bernard Ghanem, Narendra Ahuja
books