| 摘要 |
A method for performing structured learning for high-dimensional discrete graphical models includes estimating a high-order interaction neighborhood structure of each visible unit or a Markov blanket of each unit; once a high-order interaction neighborhood structure of each visible unit is identified, adding corresponding energy functions with respect to the high-order interaction of that unit into an energy function of High-order BM (HBM); and applying Maximum-Likelihood Estimation updates to learn the weights associated with the identified high-order energy functions. The system can effectively identify meaningful high-order interactions between input features for system output prediction, especially for early cancer diagnosis, biomarker discovery, sentiment analysis, automatic essay grading, Natural Language Processing, text summarization, document visualization, and many other data exploration problems in Big Data. |
