International audienceThis paper addresses the problem of dimension reduction of noisy data, more precisely the challenge to determine the dimension of the subspace where the observed signal lives in. Based on results from random matrix theory, two novel estimators of the signal dimension are proposed in this paper. Consistency of the estimators is proved in the modern asymptotic regime, where the number of parameters grows proportionally with the sample size. Experimental results show that the novel estimators are robust to noise and, moreover, they give highly accurate results in settings where standard methods fail. We apply the novel dimension estimators to several life sciences benchmarks in the context of classification, and illustrat...
In this work we present a comparative analysis of the performance of two recently proposed algorithm...
This paper is devoted to the estimation of the minimal dimension P of the state-space realizations o...
International audienceWe consider the problem of subspace estimation in situations where the number ...
International audienceThis paper addresses the problem of dimension reduction of noisy data, more pr...
The last few years have seen a great increase in the amount of data available to scientists. Dataset...
The last few years have seen a great increase in the amount of data available to scientists. Dataset...
In linear dimension reduction for a p-variate random vector x, the general idea is to find an orthog...
In linear dimension reduction for a p-variate random vector x, the general idea is to find an orthog...
AbstractMany linear dimension reduction methods proposed in the literature can be formulated using a...
This dissertation presents a new framework for extracting signal from high-dimensional data using a ...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...
The high dimensionality of some real life signals makes the usage of the most common signal processi...
We consider the problem of estimating a low-rank signal matrix from noisy measurements under the ass...
International audienceWe consider the problem of subspace estimation in situations where the number ...
In this work we present a comparative analysis of the performance of two recently proposed algorithm...
This paper is devoted to the estimation of the minimal dimension P of the state-space realizations o...
International audienceWe consider the problem of subspace estimation in situations where the number ...
International audienceThis paper addresses the problem of dimension reduction of noisy data, more pr...
The last few years have seen a great increase in the amount of data available to scientists. Dataset...
The last few years have seen a great increase in the amount of data available to scientists. Dataset...
In linear dimension reduction for a p-variate random vector x, the general idea is to find an orthog...
In linear dimension reduction for a p-variate random vector x, the general idea is to find an orthog...
AbstractMany linear dimension reduction methods proposed in the literature can be formulated using a...
This dissertation presents a new framework for extracting signal from high-dimensional data using a ...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...
Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space...
The high dimensionality of some real life signals makes the usage of the most common signal processi...
We consider the problem of estimating a low-rank signal matrix from noisy measurements under the ass...
International audienceWe consider the problem of subspace estimation in situations where the number ...
In this work we present a comparative analysis of the performance of two recently proposed algorithm...
This paper is devoted to the estimation of the minimal dimension P of the state-space realizations o...
International audienceWe consider the problem of subspace estimation in situations where the number ...