Abstract Subspace clustering assumes that the data is separable into separate subspaces; this assumption may not always hold. For such cases, we assume that, even if the raw data is not separable into subspaces, one can learn a deep representation such that the learnt representation is separable into subspaces. To achieve the intended goal, we propose to embed subspace clustering techniques (locally linear manifold clustering, sparse subspace clustering and low rank representation) into deep transform learning. The entire formulation is jointly learnt; giving rise to a new class of methods called deeply transformed subspace clustering (DTSC). To test the performance of the proposed techniques, benchmarking is performed on image clustering problems. Comparison with state-of-the-art clustering techniques shows that our formulation improves upon them.

中文翻译：

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深度变换子空间聚类

Abstract Subspace clustering 假设数据可分离到单独的子空间中；这种假设可能并不总是成立。对于这种情况，我们假设，即使原始数据不能分成子空间，也可以学习一种深度表示，使得学习到的表示可以分成子空间。为了实现预期目标，我们建议将子空间聚类技术（局部线性流形聚类、稀疏子空间聚类和低秩表示）嵌入到深度变换学习中。整个配方是共同学习的；产生了一类新的方法，称为深度变换子空间聚类（DTSC）。为了测试所提出技术的性能，对图像聚类问题进行了基准测试。与最先进的聚类技术的比较表明，我们的公式改进了它们。