Spectral clustering via ensemble deep autoencoder learning (SC-EDAE)
Spectral clustering via ensemble deep autoencoder learning (SC-EDAE)
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DOI：
10.1016/j.patcog.2020.107522
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发表时间：
2020-12-01
影响因子：
8
通讯作者：
Nadif, Mohamed
中科院分区：
文献类型：
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作者：
Affeldt, Severine;Labiod, Lazhar;Nadif, Mohamed
有几项工作研究了将联合收割机经典聚类算法和深度学习方法相结合的聚类策略。这些策略通常可以提高聚类性能，但是深度自动编码器设置问题阻碍了这些方法的鲁棒性。为了减轻超参数设置的影响，我们提出了一个模型，该模型在集成框架中结合了谱聚类和深度自动编码器的优势。我们的建议不需要任何预训练，包括以下三个步骤：从原始数据中生成各种深度嵌入，基于锚策略构建稀疏和低维的集成亲和矩阵，并应用谱聚类来获得多个深度表示共享的公共空间。虽然锚点策略确保了编码的有效合并，但各种深度表示的融合能够减轻深度网络设置问题。在各种基准数据集上的实验表明，与最先进的深度聚类方法相比，我们的方法具有潜力和鲁棒性。(C)2020爱思唯尔有限公司保留所有权利。
Several works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These strategies generally improve clustering performance, however deep autoencoder setting issues impede the robustness of these approaches. To alleviate the impact of hyperparameters setting, we propose a model which combines spectral clustering and deep autoencoder strengths in an ensemble framework. Our proposal does not require any pretraining and includes the three following steps: generating various deep embeddings from the original data, constructing a sparse and low-dimensional ensemble affinity matrix based on anchors strategy and applying spectral clustering to obtain the common space shared by multiple deep representations. While the anchors strategy ensures an efficient merging of the encodings, the fusion of various deep representations enables to mitigate the deep networks setting issues. Experiments on various benchmark datasets demonstrate the potential and robustness of our approach compared to state-of-the-art deep clustering methods. (C) 2020 Elsevier Ltd. All rights reserved.