Missing Values in Dissimilarity-Based Classification of Multi-way Data

Missing values can occur frequently in many real world situations. Such is the case of multi-way data applications, where objects are usually represented by arrays of 2 or more dimensions e.g. biomedical signals that can be represented as time-frequency matrices. This lack of attributes tends to influence the analysis of the data. In classification tasks for example, the performance of classifiers is usually deteriorated. Therefore, it is necessary to address this problem before classifiers are built. Although the absence of values is common in these types of data sets, there are just a few studies to tackle this problem for classification purposes. In this paper, we study two approaches to overcome the missing values problem in dissimilarity-based classification of multi-way data. Namely, imputation by factorization, and a modification of the previously proposed Continuous Multi-way Shape measure for comparing multi-way objects.