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In order to exploit the informative components hidden in nonnegative matrix factorization, an information theoretic learning method, termed ITNMF, is presented. Different from the existing NMF methods, the proposed method is able to handle the general objective optimization, and takes the conjugate gradient technique to enhance the iterative optimization. To tackle the null matrix factorization problem,...
Representation learning is a fundamental challenge for feature selection and plays an important role in applications such as dimension reduction, data mining and object recognition. Traditional linear representation methods, such as principal component analysis (PCA), independent component analysis (ICA) and linear discriminate analysis (LDA), have good performance on certain applications based on...
We propose a subspace learning algorithm for face recognition by directly optimizing recognition performance scores. Our approach is motivated by the following observations: 1) Different face recognition tasks (i.e., face identification and verification) have different performance metrics, which implies that there exist distinguished subspaces that optimize these scores, respectively. Most prior work...
In this paper, a novel discriminant sparse non-negative matrix factorization (DSNMF) algorithm is proposed. We derive DSNMF method from original NMF algorithm by considering both sparseness constraint and discriminant information constraint. Furthermore, projected gradient method is used to solve the optimization problem. DSNMF makes use of prior class information which is important in classification,...
Active appearance model (AAM) has been widely used in face tracking and recognition. However, accuracy and efficiency are always two main challenges with the AAM search. The paper therefore proposed a fast appearance-model based 3D face tracking algorithm to track a face appearance with significant translation, rotation, and scaling activities by using stochastic meta-descent (SMD) optimization scheme...
Faces show both global and local motions, where the former represents rigid head movements due to 3D translation and rotation and the local motion represents non-rigid deformation due to speech, or facial expressions. Although non- rigid face models can represent both types of the facial motions, they are not enough to track the facial motions correctly. The non-rigid face models have large number...
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