Abstract. We propose a method of unsupervised learning from stationary, vector-valued processes. A low-dimensional subspace is selected on the basis of a criterion which rewards da...
Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification m...
Autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoass...
In this paper, we propose a novel learned visual codebook (LVC) for 3D face recognition. In our method, we first extract intrinsic discriminative information embedded in 3D faces...
Non-negative matrix factorisation (NMF) is an unsupervised learning technique that decomposes a non-negative data matrix into a product of two lower rank non-negative matrices. Th...
Alexander Bertrand, Kris Demuynck, Veronique Stout...