论文标题
通过多视图数据部分共享半监督的深度矩阵分解
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
论文作者
论文摘要
由于可以从多种视图中描述许多现实世界数据，因此多视图学习吸引了相当大的关注。已经提出了各种方法，并成功地应用于多视图学习，通常基于矩阵分解模型。最近，它扩展到了深层结构，以利用多视图数据的层次结构信息，但是很少考虑特定于视图的功能和标签信息。为了解决这些问题，我们提出了部分共享半监督的深矩阵分解模型（PSDMF）。通过整合部分共享的深层分解结构，图形正则化和半监督回归模型，PSDMF可以通过消除不相关信息的效果来学习紧凑而判别的表示。此外，我们为PSDMF开发了有效的迭代更新算法。在五个基准数据集上进行的广泛实验表明，PSDMF比最先进的多视图学习方法更好。 MATLAB源代码可在https://github.com/libertyhhn/partallyshareddmf上找到。
Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically based on matrix factorization models. Recently, it is extended to the deep structure to exploit the hierarchical information of multi-view data, but the view-specific features and the label information are seldom considered. To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF). By integrating the partially shared deep decomposition structure, graph regularization and the semi-supervised regression model, PSDMF can learn a compact and discriminative representation through eliminating the effects of uncorrelated information. In addition, we develop an efficient iterative updating algorithm for PSDMF. Extensive experiments on five benchmark datasets demonstrate that PSDMF can achieve better performance than the state-of-the-art multi-view learning approaches. The MATLAB source code is available at https://github.com/libertyhhn/PartiallySharedDMF.
