Cost-efficient unsupervised sample selection for multivariate calibration
Indirect quantification of chemical composition through spectral measurements requires the establishment of multivariate calibration models. The reference analyses on the calibration samples typically form a major cost factor in the establishment of these multivariate models. Therefore, the aim of this study was to select the most informative calibration samples in an unsupervised way based on the spectral measurements. To this end, guidelines to address this challenge in PLSR model building have been developed. The recommendations include calculating a sample size that surpasses the model complexity by a factor of 12, performing the selection in the PCA score space spanned by a sufficiently large number of principal components and using methods such as Kennard-Stone, Puchwein, Clustering or D-optimal designs. We provide the data and methodology used in the present study for future use.
Code (1)
Tasks
ClusteringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Neural Network-Based Active Learning in Multivariate Calibration
In chemometrics, data from infrared or near-infrared (NIR) spectroscopy are often used to identify a compound or to analyze the composition of amaterial. This involves the calibration of models that predict the concentra…
Active LearningSplit Conformal Classification with Unsupervised Calibration
Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Existing methods provide remarkably strong…
Computational EfficiencyMultivariate Latent Recalibration for Conditional Normalizing Flows
Reliably characterizing the full conditional distribution of a multivariate response variable given a set of covariates is crucial for trustworthy decision-making. However, misspecified or miscalibrated multivariate mode…
Conformal PredictionmTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Multivariate time series anomaly detection (MTS-AD) is critical in domains like healthcare, cybersecurity, and industrial monitoring, yet remains challenging due to complex inter-variable dependencies, temporal dynamics,…
Anomaly DetectionBenchmarkingLanguage ModelingLanguage Modelling+4Multivariate Variational Autoencoder
Learning latent representations that are simultaneously expressive, geometrically well-structured, and reliably calibrated remains a central challenge for Variational Autoencoders (VAEs). Standard VAEs typically assume a…