Knowledge Based Factorized High Order Sparse Learning Models
AssigneeNEC Laboratories America, Inc.
InventorsRenqiang Min, Sanjay Purushotham
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Abstract
An optimization-driven sparse learning framework is disclosed to identify discriminative system components among system input features that are essential for system output prediction. In biomarker discovery, to handle the combinatorial interactions among gene or protein expression measurements for identifying interaction complexes and disease biomarkers, the system uses both single input features and high-order input feature interactions.
Claims20
A method for generating a single task learning framework to predict bio-medical interactions, comprising:
- receiving input feature vectors and grouping the features;
- factorizing weights into a sum of outer products of vectors;
- applying group L1 penalty on factors;
- and
- determining individual features and high-order interactions within groups and between groups for predicting bio-medical interactions.
A method for generating a single task learning framework to predict bio-medical interactions, comprising:
- receiving input feature vectors from bio-medical interactions;
- constructing groups over both input features and output tasks;
- grouping information among input features, pairwise relationships between outputs, and high-order interactions among input features;
- and
- determining either predictions of multiple tasks or selections of single features or groups of features for the bio-medical interactions.
A method for learning bio-medical data, comprising:
- applying weight matrix factorizations and l1/l2 regularization;
- and
- identifying discriminative high-order feature group interactions in logistic regression and large-margin models for biomarker discovery and to handle combinatorial interactions among gene/protein expression measurements for identifying interaction complexes and disease biomarkers.
Dependencies are inferred from the claim text.