What is he best known for?
The fields of study he is best known for:
- Statistics
- Algebra
- Artificial intelligence
Pierre Comon focuses on Algorithm, Blind signal separation, Independent component analysis, Tensor and Source separation.
His work in the fields of Algorithm, such as Singular value decomposition, overlaps with other areas such as Humanities.
His study in Blind signal separation is interdisciplinary in nature, drawing from both Higher-order statistics, Signal processing and Kurtosis.
His study in Independent component analysis is interdisciplinary in nature, drawing from both Infomax, FastICA and Multivariate random variable.
His Multivariate random variable research integrates issues from Independence, Mutual information and Principal component analysis.
His work carried out in the field of Tensor brings together such families of science as Algebra, Rank, Outer product and Combinatorics.
His most cited work include:
- Independent component analysis, a new concept? (6953 citations)
- Handbook of Blind Source Separation: Independent Component Analysis and Applications (1091 citations)
- Independent Component Analysis (465 citations)
What are the main themes of his work throughout his whole career to date?
Pierre Comon mostly deals with Algorithm, Tensor, Artificial intelligence, Blind signal separation and Rank.
His Algorithm research is multidisciplinary, incorporating elements of Identification, Matrix, Speech recognition, Mathematical optimization and Signal processing.
His work in Tensor tackles topics such as Multilinear map which are related to areas like Tensor and Hyperspectral imaging.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His Blind signal separation research includes themes of Contrast, Higher-order statistics, Independent component analysis, Source separation and Gaussian noise.
His Independent component analysis study deals with FastICA intersecting with Infomax.
He most often published in these fields:
- Algorithm (39.66%)
- Tensor (23.18%)
- Artificial intelligence (17.32%)
What were the highlights of his more recent work (between 2014-2021)?
- Algorithm (39.66%)
- Tensor (23.18%)
- Applied mathematics (12.85%)
In recent papers he was focusing on the following fields of study:
His main research concerns Algorithm, Tensor, Applied mathematics, Rank and Tensor.
His work on Computational complexity theory as part of general Algorithm research is frequently linked to Decomposition, thereby connecting diverse disciplines of science.
His Tensor research incorporates elements of Multilinear map, Pure mathematics, Matrix, Stationary point and Nonnegative rank.
His studies deal with areas such as Circulant matrix, Singular value decomposition, Additive white Gaussian noise, Mathematical optimization and Low-rank approximation as well as Applied mathematics.
His Rank study combines topics in areas such as Discrete mathematics, Singular value, Order and Combinatorics.
As part of the same scientific family, Pierre Comon usually focuses on Type, concentrating on Independent component analysis and intersecting with Series.
Between 2014 and 2021, his most popular works were:
- Nonnegative Tensor CP Decomposition of Hyperspectral Data (55 citations)
- Brain-Source Imaging: From sparse to tensor models (39 citations)
- Exploring Multimodal Data Fusion Through Joint Decompositions with Flexible Couplings (36 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algebra
Algorithm, Tensor, Rank, Artificial intelligence and Applied mathematics are his primary areas of study.
Particularly relevant to Computational complexity theory is his body of work in Algorithm.
The concepts of his Computational complexity theory study are interwoven with issues in Ictal, Independent component analysis and Noise reduction.
His research integrates issues of Blind signal separation, Noise, Algebraic number, Sparse approximation and Nonnegative rank in his study of Tensor.
Pierre Comon works mostly in the field of Artificial intelligence, limiting it down to topics relating to Pattern recognition and, in certain cases, Eeg data and Modality, as a part of the same area of interest.
His studies in Applied mathematics integrate themes in fields like Singular value, Singular value decomposition and Combinatorics.
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