What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Rasmus Bro mostly deals with Algorithm, Chemometrics, Data mining, Missing data and Principal component analysis.
He has included themes like Statistics, Fluorescence spectrometry, Scaling and Generalization in his Algorithm study.
His Chemometrics research integrates issues from Regression and Outlier, Artificial intelligence.
The Artificial intelligence study which covers Pattern recognition that intersects with Artifact.
His research integrates issues of Monte Carlo method, Imputation, Component and Implementation in his study of Data mining.
His work deals with themes such as Non-negative least squares, Environmental chemistry, Dissolved organic carbon, Regression analysis and Emission spectrum, which intersect with Principal component analysis.
His most cited work include:
- PARAFAC. Tutorial and applications (1935 citations)
- Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial (1279 citations)
- Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy (1250 citations)
What are the main themes of his work throughout his whole career to date?
Rasmus Bro spends much of his time researching Chemometrics, Partial least squares regression, Algorithm, Data mining and Artificial intelligence.
The concepts of his Chemometrics study are interwoven with issues in Calibration and Applied mathematics.
Rasmus Bro has researched Partial least squares regression in several fields, including Linear discriminant analysis and Principal component analysis.
His Algorithm research includes elements of Multilinear map, Identifiability and Missing data.
His studies examine the connections between Data mining and genetics, as well as such issues in Sensor fusion, with regards to Metabolomics.
His Artificial intelligence research includes themes of Machine learning, Multivariate statistics, Regression and Pattern recognition.
He most often published in these fields:
- Chemometrics (25.00%)
- Partial least squares regression (17.86%)
- Algorithm (16.27%)
What were the highlights of his more recent work (between 2013-2021)?
- Chemometrics (25.00%)
- Partial least squares regression (17.86%)
- Artificial intelligence (15.87%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Chemometrics, Partial least squares regression, Artificial intelligence, Data mining and Principal component analysis.
Chemometrics and Coupling are two areas of study in which he engages in interdisciplinary work.
Rasmus Bro's looking at Partial least squares regression as part of his Analytical chemistry and Statistics and Partial least squares regression study.
Rasmus Bro interconnects Machine learning, Local regression and Pattern recognition in the investigation of issues within Artificial intelligence.
His Data mining study combines topics in areas such as Matrix decomposition, Noise, Infrared and Sensor fusion.
His research investigates the connection between Function and topics such as Component analysis that intersect with issues in Algorithm.
Between 2013 and 2021, his most popular works were:
- Principal component analysis (887 citations)
- OpenFluor- an online spectral library of auto-fluorescence by organic compounds in the environment (311 citations)
- Fluorescence spectroscopy coupled with PARAFAC and PLS DA for characterization and classification of honey. (109 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Rasmus Bro mainly investigates Partial least squares regression, Chemometrics, Chromatography, Artificial intelligence and Data mining.
His Partial least squares regression study introduces a deeper knowledge of Statistics.
His Chemometrics study integrates concerns from other disciplines, such as Fluorescence spectroscopy, Principal component analysis and Honey samples.
His Chromatography study combines topics from a wide range of disciplines, such as Deconvolution, Raw data and Identification.
His study looks at the relationship between Artificial intelligence and fields such as Pattern recognition, as well as how they intersect with chemical problems.
His research investigates the connection between Data mining and topics such as Sensor fusion that intersect with problems in Data science, Matrix and Variation.
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