Exploiting the SVM constraints in NMF with application in eating and drinking activity recognition

A novel method is introduced for exploiting the support vector machine constraints in nonnegative matrix factorization. The notion of the proposed method is to find the projection matrix that projects the data to a low-dimensional space so that the data projections between the two classes are separated with maximum margin. Experiments were performed for the task of eating and drinking activity classification. Experimental results showed that the proposed method achieves better classification performance than the state of the art nonnegative matrix factorization and discriminant nonnegative matrix factorization followed by support vector machines classification.