Hierarchical Data Representation Model - Multi-layer NMF

In this paper, we propose a data representation model that demonstrates hierarchical feature learning using nsNMF. We extend unit algorithm into several layers. Experiments with document and image data successfully discovered feature hierarchies. We also prove that proposed method results in much better classification and reconstruction performance, especially for small number of features. feature hierarchies

Hierarchical confirmatory factor analysis of the flow state scale in exercise

In this study, we examined the factor structure and internal consistency of the Flow State Scale using responses of exercise participants.This self-report questionnaire consists of nine subscales designed to assess flow in sport and physical activity. It was administered to 1231 aerobic dance exercise participants. Confirmatory factor analyses were used to test three competing measurement models of the flow construct: a single-factor model, a nine-factor model and a hierarchical model positing a higher-order flow factor to explain the intercorrelations between the nine first-order factors. The single-factor model showed a poor fit to the data. The nine-factor model and the hierarchical model did not show an adequate fit to the data. All subscales of the Flow State Scale displayed acceptable internal consistency (alpha > 0.70), with the exception of transformation of time (alpha = 0.65). Collectively, the present results do not provide support for the tenability of the single-factor, nine-factor or hierarchical measurement models in an exercise setting