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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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Contributor : Emilie Chouzenoux <>
Submitted on : Monday, August 27, 2018 - 10:33:55 AM
Last modification on : Saturday, May 1, 2021 - 3:49:24 AM
Long-term archiving on: : Wednesday, November 28, 2018 - 1:58:46 PM


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  • HAL Id : hal-01862192, version 1


Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux. Transformed Locally Linear Manifold Clustering. EUSIPCO 2018 - 26th European Signal Processing Conference, Sep 2018, Rome, Italy. ⟨hal-01862192⟩



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