Deep Low-Rank Subspace Clustering

Deep Low-Rank Subspace Clustering

This paper is concerned with developing a novel approach to tackle the problem of subspace clustering. The approach introduces a convolutional autoencoder-based architecture to generate low-rank representations (LRR) of input data which are proven to be very suitable for subspace clustering.We propose to insert a fully-connected linear layer and its transpose between the encoder and decoder to implicitly impose a rank constraint on the learned representations. We train this architecture by minimizing a standard deep subspace clustering loss function and then recover underlying subspaces by applying a variant of spectral clustering technique. Extensive experiments on benchmark datasets demonstrate that the proposed model can not only achieve very competitive clustering results using a relatively small network architecture but also can maintain its high level of performance across a wide range of LRRs. This implies that the model can be appropriately combined with the state-of-the-art subspace clustering architectures to produce more accurate results.

深度低秩子空间聚类

本文涉及开发一种解决子空间聚类问题的新方法。该方法引入了基于卷积自动编码器的体系结构，以生成输入数据的低秩表示（LRR），事实证明，该数据非常适合于子空间聚类。.. 我们建议在编码器和解码器之间插入一个完全连接的线性层及其转置，以对学习的表示形式隐式施加等级约束。我们通过最小化标准深度子空间聚类损失函数来训练该体系结构，然后通过应用频谱聚类技术的变体来恢复基础子空间。在基准数据集上进行的大量实验表明，所提出的模型不仅可以使用相对较小的网络体系结构获得非常有竞争力的聚类结果，而且可以在各种LRR范围内保持其较高的性能水平。这意味着该模型可以与最新的子空间聚类体系结构适当地组合以产生更准确的结果。 （阅读更多）