Patent ID: 8463053
Filing Date: 2013-06-11
Classification: G06F,G06K

Abstract:
1. A method for multimodal data mining, comprising: defining a multimodal data set comprising image information; representing image information of a data object as a set of feature vectors in a feature space representing a plurality of constraints for each training example, wherein the feature vectors comprise a joint feature representation associated with Lagrange multipliers, the feature vectors being partitioned into a dual variable set comprising two partitions and having non-image representations associated with the respective image data object; clustering in the feature space to group similar features; associating a non-image representation with a respective data object based on the clustering; determining a joint feature representation of a respective data object as a mathematical weighted combination of a set of components of the joint feature representation; optimizing a weighting for a plurality of components of the mathematical weighted combination with respect to a prediction error between a predicted classification and a training classification by iteratively solving a Lagrange dual problem, with an automated data processor, by partitioning the Lagrange multipliers into an active set and an inactive set, wherein the Lagrange multiplier for a member of the active set is greater than or equal to zero and the Lagrange multiplier for a member of the inactive set is zero, the iteratively solving comprising moving members of the active set having zero-valued Lagrange multipliers to the inactive set without changing an objective function, and moving members of the inactive set to the active set which result in a decrease in the objective function; and employing the mathematical weighted combination for automatically classifying a new data object, wherein: the set of feature vectors in the feature space represents a plurality of constraints for each training example, the feature vectors comprise joint feature representation defined by Φ, having a Lagrange multiplier μ the feature vectors comprise a dual variable set μ comprising labeled examples which is decomposed into two partitions, μ said optimizing comprises iteratively solving for each member of the set: while there exist μ while there exists μ moving that variable to partition μ if no such μ