Multi-block DD-SIMCA as a high-level data fusion tool

Multi-block classification method based on the Data Driven Soft Independent Modeling of Class Analogy (DDSIMCA) is presented. A high-level data fusion approach is used for the joint analysis of data collected with the help of different analytical instruments. The proposed fusion technique is very simple and straightforward. It uses a Cumulative Analytical Signal which is a combination of outcomes of the individual classification models. Any number of blocks can be combined. Although the high-level fusion eventually leads to a rather complex model, the analysis of partial distances makes it possible to establish a meaningful relationship between the classification results and the influence of individual samples and specific tools.Two real world examples are used to demonstrate the applicability of the multi-block algorithm and the consistency of the multi-block method with its predecessor, a conventional DD-SIMCA.

Automated summary

This research presents a multi-block classification method based on the Data Driven Soft Independent Modeling of Class Analogy (DDSIMCA). A high-level data fusion approach is used to jointly analyze data collected from different analytical instruments. The proposed fusion technique is simple and straightforward, using a Cumulative Analytical Signal to combine the outcomes of individual classification models. Any number of blocks can be combined. Despite the complexity of the high-level fusion model, the analysis of partial distances allows for a meaningful relationship between classification results, individual samples, and specific tools. Two real-world examples demonstrate the applicability of the multi-block algorithm and its consistency with its predecessor, conventional DD-SIMCA.