Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where deep representation and clusters are learned jointly by optimizing a single objective function. Both strategies improve clustering performance, however the robustness of these approaches is impeded by several deep autoencoder setting issues, among which the weights initialization, the width and number of layers or the number of epochs. To alleviate the impact of such hyperparameters setting on the clustering performance, we propose a new model which combines the spectral clustering and deep autoencoder strengths in an ensemble learning framework. Extensive experiments on various benchmark datasets demonstrate the potential and robustness of our approach compared to state-of-the art deep clustering methods.
翻译:最近,一些著作研究了将传统组合算法和深层学习方法相结合的集群战略,这些方法要么采用顺序方式,即先用深层自动编码器学习深层代表,再用K手段获得集群,要么同时采用通过优化单一目标功能共同学习深层代表和集群的方法,这两种战略都提高了集群的性能,然而,这些方法的稳健性却由于若干深层自动编码器设置问题而受到阻碍,这些问题包括权重初始化、层的宽度和数量或地段的数目。为了减轻这种超分计设置对集群性能的影响,我们提出了一个新模式,将光谱组合和深层自动编码器的强项结合到一个共同学习框架中。关于各种基准数据集的广泛实验表明,与最先进的集群方法相比,我们的方法具有潜力和稳健性。