International audienceThis paper addresses the problem of robust matrix root-clustering. The considered uncertainty is the so-called norm-bounded one. The clustering regions are unions of possibly disjoint and nonsymmetric disks and half planes. The concept of ∂D-regularity is introduced. Owing to this notion, a sufficient (but not too pessimistic) LMI condition for robust matrix root-clustering is obtained
We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise grou...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
In this paper we introduce a new clustering technique called Regularity Clustering. This new techniq...
International audienceThis paper addresses the problem of robust matrix root-clustering. The conside...
International audienceThis paper considers robust stability analysis for a matrix affected by unstru...
A new sufficient condition is proposed for robust root clustering in an arbitrary subregion of the c...
International audienceThe research for robustness bounds for systems whose behaviour is described by...
Sufficient bounds for structured and unstructured uncertainties for root-clustering in a specified s...
International audienceThis paper tackles the problem of the characterization of robust pole-clusteri...
International audienceThe problem of robust matrix root-clustering against additive structured uncer...
International audienceThe problem of robust matrix root-clustering against additive structured uncer...
The Optimally Tuned Robust Improper Maximum Likelihood Estima- tor (OTRIMLE) for robust model-based ...
Subspace clustering has important and wide applica-tions in computer vision and pattern recognition....
In this paper, we study the robust subspace clustering problem, which aims to cluster the given poss...
We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method ...
We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise grou...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
In this paper we introduce a new clustering technique called Regularity Clustering. This new techniq...
International audienceThis paper addresses the problem of robust matrix root-clustering. The conside...
International audienceThis paper considers robust stability analysis for a matrix affected by unstru...
A new sufficient condition is proposed for robust root clustering in an arbitrary subregion of the c...
International audienceThe research for robustness bounds for systems whose behaviour is described by...
Sufficient bounds for structured and unstructured uncertainties for root-clustering in a specified s...
International audienceThis paper tackles the problem of the characterization of robust pole-clusteri...
International audienceThe problem of robust matrix root-clustering against additive structured uncer...
International audienceThe problem of robust matrix root-clustering against additive structured uncer...
The Optimally Tuned Robust Improper Maximum Likelihood Estima- tor (OTRIMLE) for robust model-based ...
Subspace clustering has important and wide applica-tions in computer vision and pattern recognition....
In this paper, we study the robust subspace clustering problem, which aims to cluster the given poss...
We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method ...
We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise grou...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
In this paper we introduce a new clustering technique called Regularity Clustering. This new techniq...
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