论文标题
超过深层子空间聚类网络
Overcomplete Deep Subspace Clustering Networks
论文作者
论文摘要
深度子空间聚类网络（DSC）通过使用具有完整连接层的底层深度自动编码器来利用自我表达属性，从而为无监督的子空间聚类问题提供了有效的解决方案。该方法使用输入数据的底盘表示，这使其不那么稳定，并且更依赖于预训练。为了克服这一点，我们提出了一种简单而有效的替代方法 - 超过深度子空间聚类网络（ODSC），在该网络中，我们使用过度的表示子空间聚类。在我们提出的方法中，我们将底盘和过度自动编码器网络的功能融合在一起，然后再通过自我表达层，从而使我们能够提取输入数据的更有意义，更强大的表示，以进行聚类。在四个基准数据集上的实验结果显示了在聚类误差方面，提出的方法比DSC和其他聚类方法的有效性。我们的方法也不像DSC那样依赖于应停止预训练以获得最佳性能，并且对噪声也更强大。代码 - \ href {https://github.com/jeya-maria-jose/overcomplete-deep-subspace-clustering} {https://github.com/jeya-maria-jose/jeya-maria-jose/overcomplete-complete-complete-deep-deep-deep-deep-subspace-clustering一下
Deep Subspace Clustering Networks (DSC) provide an efficient solution to the problem of unsupervised subspace clustering by using an undercomplete deep auto-encoder with a fully-connected layer to exploit the self expressiveness property. This method uses undercomplete representations of the input data which makes it not so robust and more dependent on pre-training. To overcome this, we propose a simple yet efficient alternative method - Overcomplete Deep Subspace Clustering Networks (ODSC) where we use overcomplete representations for subspace clustering. In our proposed method, we fuse the features from both undercomplete and overcomplete auto-encoder networks before passing them through the self-expressive layer thus enabling us to extract a more meaningful and robust representation of the input data for clustering. Experimental results on four benchmark datasets show the effectiveness of the proposed method over DSC and other clustering methods in terms of clustering error. Our method is also not as dependent as DSC is on where pre-training should be stopped to get the best performance and is also more robust to noise. Code - \href{https://github.com/jeya-maria-jose/Overcomplete-Deep-Subspace-Clustering}{https://github.com/jeya-maria-jose/Overcomplete-Deep-Subspace-Clustering
