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A graph regularized non-negative matrix factorization (NMF) model is proposed for image pattern discovery. Each image is represented by its histogram of visual words (i.e. bag-of-words) and the image contents are discovered by the NMF model. The graph regularization preserves the spatial closeness of visual code words in the obtained patterns, thus improving the bag-of-words representation against...
In this paper, we present a model for unsupervised pattern discovery using non-negative matrix factorization (NMF) with graph regularization. Though the regularization can be applied to many applications, we illustrate its effectiveness in a task of vocabulary acquisition in which a spoken utterance is represented by its histogram of the acoustic co-occurrences. The regularization expresses that temporally...
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