Abstract
In this paper, we present a new classification and clustering framework via structure-enforced matrix factorization which represents a large class of mathematical models appearing in many applications. We are factorizing the data as representations of several blocks, one block for each cluster or class. The signals which are best reconstructed by the same block are clustered together. The proposed framework additionally imposes incoherence in the blocks since an incoherence promoting term encourages blocks associated to different classes to be as independent as possible. In addition, the new framework is applicable both to supervised and unsupervised learning. We first illustrate the proposed framework for the MNIST digit dataset in the supervised and unsupervised way, respectively, obtaining results comparable to the state-of-the-art. We then present experiments for fully unsupervised clustering on simple texture images for instance, also yielding excellent performance.
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Xu, L., Zhou, Y., Yu, B. (2017). Classification and Clustering via Structure-enforced Matrix Factorization. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_35
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DOI: https://doi.org/10.1007/978-3-319-67777-4_35
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