News

We propose a novel computational method for representing multidimensional probability distributions through a unimodal Tucker decomposition. Motivated by social science surveys that use Likert scales, we interpret vague response categories as fuzzy sets to better capture their inherent uncertainty. This approach utilizes an alternating least squares algorithm combined with unimodal projections to ensure that the resulting factor matrices are both interpretable and mathematically compact. By restricting these matrices to unimodal shapes, the model aligns with natural human response patterns while significantly reducing the number of parameters required for data storage. Experimental results using Czech survey data demonstrate that this constrained method provides a high-quality approximation of original data tables. Ultimately, the work offers a more efficient and readable framework for analyzing complex probabilistic models in the social sciences.