The Real-Valued Model of Hierarchical Classes

We propose a non-negative real-valued model of hierarchical classes (HICLAS) for two-way two-mode data. Like the other members of the HICLAS family, the non-negative real-valued model (NNRV-HICLAS) implies simultaneous hierarchically organized classifications of all modes involved in the data. A distinctive feature of the novel model is that it yields continuous, non-negative real-valued reconstructed data, which considerably expands the application range of the HICLAS family. The expansion implies a major algorithmic challenge as it involves a move from the typical discrete optimization problems in HICLAS to a mixed discrete-continuous one. To solve this mixed discrete-continuous optimization problem, a two-stage algorithm combining a simulated annealing and an alternating local descent stage is proposed. Subsequently it is evaluated in a simulation study. Finally, the NNRVHICLAS model is applied to an empirical data set on anger.