Generalized Matrix Learning Vector Quantization Research Articles

Towards Robust Colour Texture Analysis with Limited Training Data

Texture analysis plays an important role in different domains of healthcare, agriculture, and industry, where multi-channel sensors are gaining more attention. This contribution presents an interpretable and efficient framework for texture classification and segmentation that exploits colour or channel information and does not require much data to produce accurate results. This makes such a framework well-suited for medical applications and resource-limited hardware. Our approach builds upon a distance-based generalized matrix learning vector quantization (GMLVQ) algorithm. We extend it with parametrized angle-based dissimilarity and introduce a special matrix format for multi-channel images. Classification accuracy evaluation of various model designs was performed on VisTex and ALOT data, and the segmentation application was demonstrated on an agricultural data set. Our extension of parametrized angle dissimilarity measure leads to better model generalization and robustness against varying lighting conditions than its Euclidean counterpart. The proposed matrix format for multichannel images enhances classification accuracy while reducing the number of parameters. Regarding segmentation, our method shows promising results, provided with a small class-imbalanced training data set. Proposed methodology achieves higher accuracy than prior work benchmarks and a small-scale CNN while maintaining a significantly lower parameter count. Notably, it is interpretable and accurate in scenarios where limited and unbalanced training data are available.

Intelligent support in manufacturing process selection based on artificial neural networks, fuzzy logic, and genetic algorithms: Current state and future perspectives

Technological advances, dynamic customer needs, growing uncertainty, and the imperative for sustainable development pressure manufacturing entities to enhance productivity and competitiveness. In this challenging landscape, decision-making in manufacturing process selection is critical. Adopting intelligent support is essential for balancing performance and costs through optimal process selection. Through a comprehensive review of 93 studies published between 2013 and 2023, this paper aims to provide a profound understanding of intelligent support in manufacturing process selection. The findings, which indicate significant interest in intelligent methodologies for manufacturing process selection, are of great importance. Fuzzy logic is prevalent in additive manufacturing due to its ability to handle complex and imprecise data. At the same time, artificial neural networks are favored in conventional manufacturing for leveraging extensive historical data. Genetic algorithms are primarily used for optimization challenges. As manufacturing evolves with new technologies and complex materials, this paper advocates adopting a generalized matrix learning vector quantization neural network for efficient and intelligent process selection in additive and conventional approaches due to its capacity to leverage historical data and handle complex and high dimensional data.

OR29-2 Comprehensive Steroid and Global Metabolome Analysis by Mass Spectrometry and Machine Learning to Understand Metabolic Risk in Benign Adrenal Tumours With Mild Autonomous Cortisol Secretion

Abstract Background Benign adrenal tumours are found in 3-10% of adults and can be non-functioning (NFAT) or associated with adrenal hormone excess. Analysing 1305 prospectively recruited patients with benign adrenal tumours, we recently demonstrated that 45% of patients had mild autonomous cortisol secretion (MACS), i.e. biochemical evidence of cortisol excess without distinct signs of Cushing syndrome (CS). We found that MACS increases the prevalence and severity of type 2 diabetes and hypertension and primarily affects women (Ann Int Med. 2022 Doi: 10.7326/M21-1737). Here we analysed the cohort's steroid metabolome and non-targeted global metabolome to reveal underlying metabolic processes. Methods We analysed 24-h urine samples from 1305 patients (649 NFAT, 591 MACS, 65 CS) using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) multi-steroid profiling assay. In addition, we performed non-targeted serum metabolome analysis in a representative sub-cohort (104 NFAT, 140 MACS, 47 CS) employing two complementary LC-MS assays, HILIC and C18-lipidomics. The steroid and global metabolome data were analysed by two supervised machine learning approaches, generalized matrix learning vector quantization and ordinal regression, to identify the most relevant metabolic changes. Findings Urine steroid metabolome analysis revealed an increase in glucocorticoid excretion from NFAT over MACS to CS, whereas androgen excretion decreased. Increased glucocorticoid metabolites were also the major differentiators between MACS patients with and without type 2 diabetes and hypertension, respectively. Lipidome analysis by machine learning identified glycerophospholipids, lysoglycerophospholipids, triacylglycerides, ceramides, sphingolipids, and acylcarnitines as the most relevant metabolite classes exhibiting gradually progressive changes with increasing cortisol excess (NFATInterpretation: We show a gradual change in the lipidome towards lipotoxicity with increasing cortisol excess. MACS patients with type 2 diabetes and hypertension had higher glucocorticoid output than other MACS patients, suggestive of a causative contribution of cortisol excess to their increased cardiometabolic burden. Observed changes may hold promise for risk stratification in MACS, a highly relevant and previously largely overlooked metabolic risk condition. Presentation: Tuesday, June 14, 2022 10:00 a.m. - 10:15 a.m.

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Background and Objectives18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. MethodsWe applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. ResultsThe GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). ConclusionIn this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.

Detection of extragalactic Ultra-compact dwarfs and Globular Clusters using Explainable AI techniques

Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to study galaxies mass assembly, formation and evolution. However, in the lack of spectroscopic information detecting UCDs/GCs using imaging data is very uncertain. Here, we aim to train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namely u, g, r, i, J and Ks. The classes of objects are highly imbalanced which is problematic for many automatic classification techniques. Hence, we employ Synthetic Minority Over-sampling to handle the imbalance of the training data. Then, we compare two classifiers, namely Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able to identify UCDs/GCs with a precision and a recall of >93% and provide relevances that reflect the importance of each feature dimension for the classification. Both methods detect angular sizes as important markers for this classification problem. While it is astronomical expectation that color indices of u−i and i−Ks are the most important colors, our analysis shows that colors such as g−r are more informative, potentially because of higher signal-to-noise ratio. Besides the excellent performance the LGMLVQ method allows further interpretability by providing the feature importance for each individual class, class-wise representative samples and the possibility for non-linear visualization of the data as demonstrated in this contribution. We conclude that employing machine learning techniques to identify UCDs/GCs can lead to promising results. Especially transparent methods allow further investigation and analysis of importance of the measurements for the detection problem and provide tools for non-linear visualization of the data.

Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices.

Learning vector quantization (LVQ) is a simple and efficient classification method, enjoying great popularity. However, in many classification scenarios, such as electroencephalogram (EEG) classification, the input features are represented by symmetric positive-definite (SPD) matrices that live in a curved manifold rather than vectors that live in the flat Euclidean space. In this article, we propose a new classification method for data points that live in the curved Riemannian manifolds in the framework of LVQ. The proposed method alters generalized LVQ (GLVQ) with the Euclidean distance to the one operating under the appropriate Riemannian metric. We instantiate the proposed method for the Riemannian manifold of SPD matrices equipped with the Riemannian natural metric. Empirical investigations on synthetic data and real-world motor imagery EEG data demonstrate that the performance of the proposed generalized learning Riemannian space quantization can significantly outperform the Euclidean GLVQ, generalized relevance LVQ (GRLVQ), and generalized matrix LVQ (GMLVQ). The proposed method also shows competitive performance to the state-of-the-art methods on the EEG classification of motor imagery tasks.

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ).Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists.Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients.Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully.

Positive and negative parenting in conduct disorder with high versus low levels of callous–unemotional traits

Less is known about the relationship between conduct disorder (CD), callous-unemotional (CU) traits, and positive and negative parenting in youth compared to early childhood. We combined traditional univariate analyses with a novel machine learning classifier (Angle-based Generalized Matrix Learning Vector Quantization) to classify youth (N = 756; 9-18 years) into typically developing (TD) or CD groups with or without elevated CU traits (CD/HCU, CD/LCU, respectively) using youth- and parent-reports of parenting behavior. At the group level, both CD/HCU and CD/LCU were associated with high negative and low positive parenting relative to TD. However, only positive parenting differed between the CD/HCU and CD/LCU groups. In classification analyses, performance was best when distinguishing CD/HCU from TD groups and poorest when distinguishing CD/HCU from CD/LCU groups. Positive and negative parenting were both relevant when distinguishing CD/HCU from TD, negative parenting was most relevant when distinguishing between CD/LCU and TD, and positive parenting was most relevant when distinguishing CD/HCU from CD/LCU groups. These findings suggest that while positive parenting distinguishes between CD/HCU and CD/LCU, negative parenting is associated with both CD subtypes. These results highlight the importance of considering multiple parenting behaviors in CD with varying levels of CU traits in late childhood/adolescence.

Ordinal regression based on learning vector quantization

Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules. Moreover, in our approach the bandwidth of the prototype weights is automatically adapted. Empirical investigation on a number of datasets reveals that overall the proposed approach tends to have superior out-of-sample performance, when compared to alternative ordinal regression methods.

Identifying HIV Associated Neurocognitive Disorder Using Large-Scale Granger Causality Analysis on Resting-State Functional MRI.

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a “Learning with privileged information” approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Incorporating Privileged Information Through Metric Learning

In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. The vast majority of existing approaches simply ignore such auxiliary (privileged) knowledge. Recently a new paradigm-learning using privileged information-was introduced in the framework of SVM+. This approach is formulated for binary classification and, as typical for many kernel-based methods, can scale unfavorably with the number of training examples. While speeding up training methods and extensions of SVM+ to multiclass problems are possible, in this paper we present a more direct novel methodology for incorporating valuable privileged knowledge in the model construction phase, primarily formulated in the framework of generalized matrix learning vector quantization. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. Hence, unlike in SVM+, any convenient classifier can be used after such metric modification, bringing more flexibility to the problem of incorporating privileged information during the training. Experiments demonstrate that the manipulation of an input space metric based on privileged data improves classification accuracy. Moreover, our methods can achieve competitive performance against the SVM+ formulations.

Texture feature ranking with relevance learning to classify interstitial lung disease patterns

The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images. After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. 65 texture features were extracted from gray-level co-occurrence matrices (GLCMs). These features were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. The classification performance for different feature subsets was calculated for a k-nearest-neighbor (kNN) and a random forests classifier (RanForest), and support vector machines with a linear and a radial basis function kernel (SVMlin and SVMrbf). For all classifiers, feature sets selected by the relevance ranking assessed by GMLVQ had a significantly better classification performance (p<0.05) for many texture feature sets compared to the MI approach. For kNN, RanForest, and SVMrbf, some of these feature subsets had a significantly better classification performance when compared to the set consisting of all features (p<0.05). While this approach estimates the relevance of single features, future considerations of GMLVQ should include the pairwise correlation for the feature ranking, e.g. to reduce the redundancy of two equally relevant features.

Limited Rank Matrix Learning, discriminative dimension reduction and visualization

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method.

Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors

Context:Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.Objective:Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy.Design:Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19–84 yr) and 45 ACC patients (20–80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26–201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids.Results:Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers.Conclusions:Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.