Deep Tensor CCA for Multi-View Learning
Deep Tensor CCA for Multi-View Learning
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DOI：
10.1109/tbdata.2021.3079234
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发表时间：
2020-05
影响因子：
7.2
通讯作者：
Hok Shing Wong;L. xilinx Wang;R. Chan;T. Zeng
中科院分区：
文献类型：
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作者：
Hok Shing Wong;L. xilinx Wang;R. Chan;T. Zeng
我们提出了深度张量典型相关分析（DTCCA），这是一种学习数据的多个视图（两个以上）的复杂非线性变换的方法，使得所得到的表示在高阶中线性相关。给定多个视图的高阶相关性由协方差张量建模，这不同于大多数CCA公式仅依赖于成对相关性。通过最大化高阶典型相关来联合学习每个视图的变换参数。为了解决这个问题，我们将其转化为秩1近似的最佳和，这可以有效地解决现有的张量分解方法。DTCCA是张量CCA（TCCA）通过深度网络的非线性扩展。与核TCCA相比，DTCCA不仅可以处理任意维数的输入数据，而且不需要维护用于计算任何给定数据点的表示的训练数据。因此，DTCCA作为一个统一的模型，可以有效地克服TCCA的可扩展性问题，无论是高维多视图数据或大量的视图，它也自然扩展TCCA学习非线性表示。在四个多视图数据集上的实验证明了该方法的有效性。
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise correlations. Parameters of transformations of each view are jointly learned by maximizing the high-order canonical correlation. To solve the resulting problem, we reformulate it as the best sum of rank-1 approximation, which can be efficiently solved by existing tensor decomposition method. DTCCA is a nonlinear extension of tensor CCA (TCCA) via deep networks. Comparing with kernel TCCA, DTCCA not only can deal with arbitrary dimensions of the input data, but also does not need to maintain the training data for computing representations of any given data point. Hence, DTCCA as a unified model can efficiently overcome the scalable issue of TCCA for either high-dimensional multi-view data or a large amount of views, and it also naturally extends TCCA for learning nonlinear representation. Extensive experiments on four multi-view data sets demonstrate the effectiveness of the proposed method.