Transformed Locally Linear Manifold Clustering

Abstract : Transform learning is a relatively new analysis formulation for learning a basis to represent signals. This work incorporates the simplest subspace clustering formulation – Locally Linear Manifold Clustering, into the transform learning formulation. The core idea is to perform the clustering task in a transformed domain instead of processing directly the raw samples. The transform analysis step and the clustering are not done piecemeal but are performed jointly through the formulation of a coupled minimization problem. Comparison with state-of-the-art deep learning-based clustering methods and popular subspace clustering techniques shows that our formulation improves upon them.
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Communication dans un congrès
26th European Signal Processing Conference, Sep 2018, Rome, Italy. 2018, Proceedings of the 26th European Signal Processing Conference (EUSIPCO 2018)
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https://hal.archives-ouvertes.fr/hal-01862192
Contributeur : Emilie Chouzenoux <>
Soumis le : lundi 27 août 2018 - 10:33:55
Dernière modification le : vendredi 31 août 2018 - 01:06:55
Document(s) archivé(s) le : mercredi 28 novembre 2018 - 13:58:46

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PID5423331.pdf
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  • HAL Id : hal-01862192, version 1

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Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux. Transformed Locally Linear Manifold Clustering. 26th European Signal Processing Conference, Sep 2018, Rome, Italy. 2018, Proceedings of the 26th European Signal Processing Conference (EUSIPCO 2018). 〈hal-01862192〉

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