Читать книгу Multiblock Data Fusion in Statistics and Machine Learning - Tormod Næs - Страница 38

Many goals of multiblock data analysis can be envisaged. In current practice, these goals are usually implicit. By making these goals explicit it will become necessary to also make explicit the global optimisation criterion or, when such a criterion is difficult to formulate, to carefully think about the whole data analysis procedure and which method to choose. Several general goals will be discussed briefly.

Exploratory analysis:One of the most obvious goals of multiblock data analysis is exploration which is a part of unsupervised analysis. By plotting the weights, scores, and loadings, summaries of the data are obtained which can be interpreted and maybe further analysed using visualisation tools.Predictive models:Another obvious goal is to try to predict the variation in one data block using several other data blocks; this is a part of supervised analysis. The idea is then that using multiple predictive blocks gives a better prediction for future samples.Finding topologies:In the case of complex data, data blocks can be placed in different relationships. The arrangement of blocks as dependent or independent may be a purpose of the analysis. We call such an arrangement a topology. In that case, it would be useful to have a strategy for deciding on the topology that fits the data best.Common versus distinct variation:There can be common and distinct variation in the multiple data blocks (see Section 1.8). This separation into types of variation greatly simplifies subsequent interpretation of the results.Treatment effects:The effect of a treatment can be measured in different blocks of data. The interest is usually what the main effect of a treatment is on measurements in the different blocks of data.Individual differences:Apart from group differences, also individual differences are useful. This can be for personalised medicine or nutritional interventions or consumer behaviour. Multiblock data analysis may help to find such differences and thereby facilitate population stratification and sub-typing.Mixed goals:In real-life applications, a mixture of goals is usually present. It may be that a treatment has been given which expresses itself differently in the common and distinct variation. Moreover, interest may be in the main effects of treatments but also on individual treatment effect differences.