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Sparse coding has been popularly used as an effective data represen-tation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional s...
Introduction of the so called K-means features caused significant discussion in the deep learning community. Despite their simplicity, these features have achieved state of the art performance on seve...
We define and discuss the first sparse coding algorithm based on closed-form EM updates and continuous latent variables. The underlying generativemodel consists of a flexibly parameterized ‘spike-an...
This paper presents a general coding method where data in a Hilbert space are represented by finite dimensional coding vectors. The method is based on empirical risk minimization within a certain cl...
In a general fractional factorial design, the n-levels of a factor are coded by the n-th roots of the unity. This device allows a full generalization to mixed-level designs of the theory of the poly...

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