Multivariate adaptation and classification method for characteristic sample properties

Characteristic properties of samples can be measured by spectrometers, cameras or other applicable equipment. To achieve meaningful classification results with a user‐friendly arrangement of the overall system, a new approach is pursued in which principally unaltered input data are reduced to their essential content using a Wavelet transform and are refined with a special smoothing method in such a manner that certain dimension‐reducing techniques can also be employed in a numerically stable way for discontinuous data sets as they occur, for example, in classification tasks. The introduced multivariate adaptive embedding (MAE) process acts as a universal approximator in the adaptation phase, to a very large extent without iterations and parameter adjustments, and deduces a redundancy‐free model with which untrained input data with outstanding generalization properties, in terms of an application, can be processed in the application phase. While taking advantage of the proximity relationships of the data points, the entire information is mapped into a low‐dimensional coordinate system using a supervised learning process and is scaled and adapted to the respective application using an unsupervised learning process. This approach allows classification of highly related and confused data as they may occur in identification/classification setups for bacteria and other substances using spectroscopic methods.