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This work is devoted to elaboration on the idea to use block termdecomposition for group data analysis and to raise the possibility of modellinggroup activity with (Lr, 1) and Tucker blocks. A new generalization of blocktensor decomposition was considered in application to group data analysis.Suggested approach was evaluated on multilabel classification task for a set ofimages. This contribution also reports results of investigation on clusteringwith proposed tensor models in comparison with known matrix models, namelycommon orthogonal basis extraction and group independent component analysis.

Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions