In this work, we propose and evaluate a Higher Order Singular Value Decomposition (HOSVD) of a tensor as a means to classify player behavior and adjust game difficulty dynamically. Applying this method to player data collected during a plethora of game sessions resulted in a reduction of the dimensionality of the classification problem and a robust classification of player behavior. Simultaneously HOSVD was able to perform significant data compression without significant reduction as regards to the accuracy of the classification outcome and furthermore, was able to alleviate the data sparseness problem common within data collected from game sessions.