平衡无监督张量挖掘的可解释性和预测准确性
Balancing Interpretability and Predictive Accuracy for Unsupervised Tensor Mining
摘要：
PARAFAC张量分解在探索性多方面数据挖掘场景中获得了越来越大的成功。一个主要的挑战仍然是估计潜在因素的数量（即，排名）的分解，这产生高质量的，可解释的结果。在此之前，我们已经提出了一种自动张量挖掘方法，该方法利用了化学计量学领域的一个众所周知的质量启发式算法，即核心一致性诊断（CORCONDIA），以自动确定PARAFAC分解的排名。在这项工作中，我们开始探索1）结果的可解释性/质量（如CORCONDIA所表示的）和2）结果的预测准确性之间的权衡，以进一步提高秩估计质量。我们的初步结果表明，在权衡利益排名估计中取得良好的平衡。
英文摘要：
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 yields high-quality, interpretable results. Previously, we have proposed 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. In this work we set out to explore the trade-off between 1) the interpretability/quality of the results (as expressed by CORCONDIA), and 2) the predictive accuracy of the results, in order to further improve the rank estimation quality. Our preliminary results indicate that striking a good balance in that trade-off benefits rank estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
