Zhang Bin, Qiang Qianyao, Wang Fei, Nie Feiping
IEEE Trans Image Process. 2021;30:4143-4156. doi: 10.1109/TIP.2021.3062692. Epub 2021 Apr 9.
Faced with the increasing data diversity and dimensionality, multi-view dimensionality reduction has been an important technique in computer vision, data mining and multi-media applications. Since collecting labeled data is difficult and costly, unsupervised learning is of great significance. Generally, it is crucial to explore the complementarity or independence of different feature spaces in multi-view learning. How to find a low-dimensional subspace to preserve the intrinsic structure of original unlabeled high-dimensional multi-view data is still challenging. In addition, noises and outliers always appear in real data. In this study, we propose a novel model called flexible multi-view unsupervised graph embedding (FMUGE). A flexible regression residual term is introduced so that the strict linear mapping is relaxed, new-coming data and noises are better handled, and the raw data negotiate with the learned low-dimensional representation in the procedure. To ensure the consistency among multiple views, FMUGE adaptively weights different features and fuses them to get an optimal multi-view consensus similarity graph, which assists high-quality graph embedding. We propose an efficient alternating iterative algorithm to optimize the proposed model. Finally, experimental results on synthetic and benchmark datasets show the significant improvement of FMUGE over the state-of-the-art methods.
面对日益增长的数据多样性和维度，多视图降维已成为计算机视觉、数据挖掘和多媒体应用中的一项重要技术。由于收集有标签数据既困难又昂贵，无监督学习具有重要意义。一般来说，探索多视图学习中不同特征空间的互补性或独立性至关重要。如何找到一个低维子空间来保留原始无标签高维多视图数据的内在结构仍然具有挑战性。此外，噪声和离群值总是出现在真实数据中。在本研究中，我们提出了一种名为灵活多视图无监督图嵌入（FMUGE）的新模型。引入了一个灵活的回归残差项，从而放宽了严格的线性映射，更好地处理新数据和噪声，并且原始数据在该过程中与学习到的低维表示进行协商。为确保多个视图之间的一致性，FMUGE对不同特征进行自适应加权并融合它们，以获得一个最优的多视图共识相似性图，这有助于高质量的图嵌入。我们提出了一种有效的交替迭代算法来优化所提出的模型。最后，在合成数据集和基准数据集上的实验结果表明，FMUGE相对于现有方法有显著改进。
