Abstract:
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 p...Show MoreMetadata
Abstract:
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.
Published in: 2013 IEEE International Conference on Image Processing
Date of Conference: 15-18 September 2013
Date Added to IEEE Xplore: 13 February 2014
Electronic ISBN:978-1-4799-2341-0