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 i, y for each constraint to form the Lagrangian, wherein i and j denote different elements of a respective set, y i is an annotation and a member of the set Y, x i and x j are each annotations and members of the set X, superscript T denotes a transpose matrix, n is the number of elements in the respective set, y represents a prediction of y i from the set Y i , {tilde over (y)} is an operator of y, α=Σ i, y μ i, y Φ i,y i , y , l( y ,y i ) is a loss function defined as the number of the different entries in vectors y and y i , Φ i,yi, y =Φ i (y i )−Φ i ( y ), and a kernel function K((x i , y ),(x j ,{tilde over (y)}))=<Φ i,yi, y , Φ j,yj, y >, the feature vectors being partitioned into a dual variable set μ comprising two partitions, μ B and μ N and non-image representations S associated with the respective image data object, the dual variable set μ having i examples such that μ=[μ 1 T. .. μ n T ] T and S=[S 1 T. .. S n T ] T , wherein the lengths of μ and S are the same, and A i is defined as a vector which has the same length as that of μ, where A i , y =1 and A j , y =0 for j≠i, such that A=[A 1. .. A n ] T , matrix D represents a kernel matrix where each entry is K((x i , y ), (x j ,{tilde over (y)})), and C represents a vector where each entry is a constant C; the feature vectors comprise a dual variable set μ comprising labeled examples which is decomposed into two partitions, μ B and μ N ; and said optimizing comprises iteratively solving for each member of the set: min ⁢ 1 2 ⁢ μ T ⁢ D ⁢ ⁢ μ - μ T ⁢ S ; ⁢ ⁢ and s. t. A ⁢ ⁢ μ ⪯ C μ B ≽ 0 , ⁢ μ N = 0 while there exist μ i, y ∈μ B such that μ i, y =0, moving that variable to partition μ N ; while there exists μ i, y ∈μ N satisfying the condition 
 ∃ i, Σ y μ i, y <C 
 ∃μ i, y ∈μ N , α T Φ i,y i , y −l ( y ,y i )<0, moving that variable to partition μ B ; and if no such μ i, y ∈μ N exists, ceasing an iteration.