Data Fusion with Parafac and Transfer of Stacked Local Classifiers

Some data analysis methods yield poor or only adequate information on their own but with data fusion, multiple datasets can be merged to possibly yield more information than when used alone. Data fusion can even be used to merge reduced representations of different parts of the same dataset. Data fusion yields improved results in situations where each set of data to be merged contains information unique from each other. Stacked Partial Least Squares Discriminant-Based Classification (SPLSDA) transfer attempts to use data fusion to aid in applying previous analysis to new data. Data transfer or model transfer allows for use of datasets taken under different conditions or on different instruments, if this variation can be accounted for. SPLSDA transfer is based upon a previously developed classification transfer approach which uses a reduced dimensional representation for each different section of the data, in order to classify new samples taken under different conditions. A similar method is Interval Partial Least Squares (IPLS) with the exception that these intervals collectively cover all of the data. Only some of the data in each section is used as much of the data contains very redundant information or is uninformative