论文标题
通过自动编码器和注意的多视图子空间自适应学习
Multi-view Subspace Adaptive Learning via Autoencoder and Attention
论文作者
论文摘要
多视图学习可以更全面地涵盖数据样本的所有功能，因此多视图学习吸引了广泛的关注。传统的子空间聚类方法，例如稀疏子空间聚类（SSC）和低级别子空间聚类（LRSC），将单个视图的亲和力矩阵聚类，因此忽略了视图之间融合的问题。在我们的文章中，我们提出了一种基于注意力和自动编码器（MSALAA）的新的多览亚空间自适应学习。该方法结合了深层自动编码器和一种使多视图稀疏稀疏子空间聚类（MLRSSC）中各种视图的自我表述的方法，这不仅可以提高非线性拟合的能力，而且还可以符合一致性和互补学习的原理。我们从经验上观察到对六个现实生活数据集的现有基线方法的显着改善。
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between views. In our article, we propose a new Multiview Subspace Adaptive Learning based on Attention and Autoencoder (MSALAA). This method combines a deep autoencoder and a method for aligning the self-representations of various views in Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC), which can not only increase the capability to non-linearity fitting, but also can meets the principles of consistency and complementarity of multi-view learning. We empirically observe significant improvement over existing baseline methods on six real-life datasets.
