Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions
This work addresses group data analysis in machine learning, but appears incremental as it builds on existing tensor decomposition methods.
The authors tackled the problem of analyzing group data by proposing a new generalization of block tensor decomposition using (Lr,1) and Tucker blocks, which they evaluated on a multilabel image classification task and compared with existing matrix models for clustering.
This work is devoted to elaboration on the idea to use block term decomposition for group data analysis and to raise the possibility of modelling group activity with (Lr, 1) and Tucker blocks. A new generalization of block tensor decomposition was considered in application to group data analysis. Suggested approach was evaluated on multilabel classification task for a set of images. This contribution also reports results of investigation on clustering with proposed tensor models in comparison with known matrix models, namely common orthogonal basis extraction and group independent component analysis.