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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for performing structured learning for high-dimensional discrete graphical models, comprising: 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. 2. The method of claim 1, comprising determining an energy function of an HBM to have a combination of different orders of feature interactions up to a maximum order. 3. The method of claim 1, comprising applying sparsity constraints on feature interaction terms to construct a sparse model. 4. The method of claim 1, wherein the learning for the HBM is decoupled into two steps. 5. The method of claim 1, comprising applying high-order interaction neighborhood estimation and interaction weight learning. 6. The method of claim 1, comprising organizing the search space within a tree structure and a block-wise expansion of possible interactions conforming to the tree structure. 7. The method of claim 1, comprising applying a sparsehigh-order logistic regression method (shooter) for identifying interpretable high-order feature interactions and to determine an energy function of an SHBM. 8. The method of claim 7, comprising greedily exploring structures among feature interactions by solving a set of l1-regularized logistic regression problems. 9. The method of claim 7, comprising applying different sampling algorithms that scale to large number of features and interactions to learn interaction weights within an SHBM for an energy function determined by shooter. 10. The method of claim 1, comprising determining a joint probability distribution of a configuration v as: p  ( v ) = 1 Z  exp  ( - E  ( v ) ) , where Z=Σuexp(−E(u)) is the partition function and energy E(v) is: - E  ( v ) = ∑ ij  W ij  v i  v j + ∑ i  b i  v i , where bi is a bias on unit vi, and Wij is a connection weight between unit vi and vj. 11. A system for performing structured learning for high-dimensional discrete graphical models, comprising: a processor to execute computer code; computer code for estimating a high-order interaction neighborhood structure of each visible unit or a Markov blanket of each unit; computer code for adding corresponding energy functions with respect to the high-order interaction of that unit into an energy function of High-order BM (HBM) once a high-order interaction neighborhood structure of each visible unit is identified; and computer code for applying Maximum-Likelihood Estimation updates to learn the weights associated with identified high-order energy functions. 12. The system of claim 11, comprising computer code for determining an energy function of an HBM to have a combination of different orders of feature interactions up to a maximum order. 13. The system of claim 11, comprising computer code for applying sparsity constraints on feature interaction terms to construct a sparse model. 14. The system of claim 11, wherein the computer code for learning for the HBM is decoupled into two steps. 15. The system of claim 11, comprising computer code for applying high-order interaction neighborhood estimation and interaction weight learning. 16. The system of claim 11, comprising computer code for organizing the search space within a tree structure ands a block-wise expansion of possible interactions conforming to the tree structure 17. The system of claim 11, comprising computer code for applying a sparsehigh-order logistic regression method (shooter) for identifying interpretable high-order feature interactions and to determine an energy function of an SHBM. 18. The system of claim 17, comprising computer code for greedily exploring structures among feature interactions by solving a set of l1-regularized logistic regression problems. 19. The system of claim 11, comprising computer code for applying different sampling algorithms that scale to large number of features and interactions to learn interaction weights within an SHBM for an energy function determined by shooter. 20. The system of claim 11, comprising computer code for determining a joint probability distribution of a configuration v as: p  ( v ) = 1 Z  exp  ( - E  ( v ) ) , where Z=Σuexp(−E(u)) is the partition function and energy E(v) is: - E  ( v ) = ∑ ij  W ij  v i  v j + ∑ i  b i  v i , where bi is a bias on unit vi, and Wij is a connection weight between unit vi and vj.