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We consider the problem of learning graphs in a sparse multiclass support vector machines framework. For such a problem, sparse graph penalty is useful to select the significant features and interpret the results. Classical ℓ1-norm learns a sparse solution without considering the structure between the features. In this paper, a structural knowledge is encoded as directed acyclic graph and a graph...
Over the past decades, face recognition has been a problem of critical interest in the machine learning and signal processing communities. However, conventional approaches such as eigenfaces do not protect the privacy of user data, which is emerging as an important design consideration in today's society. In this work, we leverage a supervised-learning subspace projection method called Discriminant...
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