Abstract Purpose Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias. Methods We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain $$^{18}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mn>18</mml:mn> <…

<title>Abstract</title> Purpose: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias. Methods: We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) PET scans. The center difference is learned by applying IRMA …

We propose a bias detection and mitigating scheme for data in the context of classification tasks based on learning vector quantizers (LVQ) as classifier. For this purpose generalized LVQ endowed with an advanced matrix adaptation scheme is used for bias detection. The bias removal from data is realized applying a nullspace data projection using the adjusted matrix. The usefulness of the approach is demonstrated and illustrated in terms of two real world datasets.

We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantization for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative sub…

We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative subs…