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Deep Embedded SOM: Joint Representation Learning and Self-Organization
ESANN 2019 2019
• FlorentF9/DESOM
• 
In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map.
DIMENSIONALITY REDUCTION REPRESENTATION LEARNING SELF-ORGANIZED CLUSTERING
Robust Graph Learning from Noisy Data
The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA.
GRAPH CONSTRUCTION GRAPH LEARNING IMAGE/DOCUMENT CLUSTERING IMAGE SHADOW REMOVAL OBJECT RECOGNITION SHADOW REMOVAL VIDEO BACKGROUND SUBTRACTION