Balancing Interpretability and Predictive Accuracy for Unsupervised Tensor Mining

The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which is known to yield high-quality, interpretable results. Previously, AutoTen, an automated tensor mining method which leverages a well-known quality heuristic from the field of Chemometrics, the Core Consistency Diagnostic (CORCONDIA), in order to automatically determine the rank for the PARAFAC decomposition, was proposed. In this work, building upon AutoTen, we set out to explore the trade-off between 1) the interpretability of the results (as expressed by CORCONDIA), and 2) the predictive accuracy of the decomposition, towards improving rank estimation quality. Our preliminary results indicate that striking a good balance in that trade-off yields high-quality rank estimation, towards achieving unsupervised tensor mining.