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Improved Deep Embedded Clustering with Local Structure Preservation
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Deep clustering learns deep feature representations that favor clustering task using neural networks. Some pioneering work proposes to simultaneously learn embedded features and perform clustering by explicitly defining a clustering oriented loss. Though promising performance has been demonstrated in various applications, we observe that a vital ingredient has been overlooked by these work that the defined clustering loss may corrupt feature space, which leads to non-representative meaningless
doi:10.24963/ijcai.2017/243
dblp:conf/ijcai/GuoGLY17
fatcat:fyprnqbqpfemzacs7ynxdfdbga