Concept

Non-negative matrix factorization

Publications associées (37)

Scalable Metric Learning via Weighted Approximate Rank Component Analysis

We are interested in the large-scale learning of Mahalanobis distances, with a particular focus on person re-identification. We propose a metric learning formulation called Weighted Approximate Rank Component Analysis (WARCA). WARCA optimizes the precision ...

Springer Int Publishing Ag2016

Inverse Rendering of Lambertian Surfaces Using Subspace Methods

Quy Ha Nguyen, Minh Do

We propose a vector space approach for inverse rendering of a Lambertian convex object with distant light sources. In this problem, the texture of the object and arbitrary lightings are both to be recovered from multiple images of the object and its 3D mod ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Multivariate Modeling and Chemometric Resolution of Mixture Spectra in Dynamic Reaction Systems

Julien Léo Billeter

In chemometrics, two very different classes of mathematical tools, self-modeling curve resolution and hard-modeling (first-principles) methods, have been developed to resolve pure component concentration profiles and spectra from mixture spectra recorded o ...

2014

Scalable Collaborative Bayesian Preference Learning

Matthias Seeger, Mohammad Emtiyaz Khan, Young Jun Ko

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan

We study the statistics of wavelet coefficients of non-Gaussian images, focusing mainly on the behaviour at coarse scales. We assume that an image can be whitened by a fractional Laplacian operator, which is consistent with an

∥ω∥ ^{ -\gamma }

spectral ...

IEEE2014

Understanding and improving relational matrix factorization in recommender systems

Boi Faltings, Li Pu

Matrix factorization techniques such as the singular value decomposition (SVD) have had great success in recommender systems. We present a new perspective of SVD for constructing a latent space from the training data, which is justified by the theory of hy ...

ACM Press2013

Multiclass Latent Locally Linear Support Vector Machines

Barbara Caputo, Marco Fornoni

Kernelized Support Vector Machines (SVM) have gained the status of o-the-shelf classiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grow ...

2013

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...

Institute of Electrical and Electronics Engineers2012

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

SPARSE NON-NEGATIVE DECOMPOSITION OF SPEECH POWER SPECTRA FOR FORMANT TRACKING

Jean-Philippe Thiran

Many works on speech processing have dealt with auto-regressive (AR) models for spectral envelope and formant frequency estimation, mostly focusing on the estimation of the AR parameters. However, it is also interesting to be able to directly estimate the ...

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Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan

∥ω∥ ^{ -\gamma }

spectral ...

IEEE2014