Canonical Correlation Analysis (CCA) models can extract informative correlated representations from multimodal unlabelled data. Despite their success, CCA models may break if the number of variables exceeds the number of samples. We propose Deep Gated-CCA, a method for learning correlated representations based on a sparse subset of variables from two observed modalities. The proposed procedure learns two non-linear transformations and simultaneously gates the input variables to identify a subset of most correlated variables. The non-linear transformations are learned by training two neural networks to maximize a shared correlation loss defined based on their outputs. Gating is obtained by adding an approximate $\ell_0$ regularization term applied to the input variables. This approximation relies on a recently proposed continuous Gaussian based relaxation for Bernoulli variables which act as gates. We demonstrate the efficacy of the method using several synthetic and real examples. Most notably, the method outperforms other linear and non-linear CCA models.

翻译：Canonic Connectrical Consulations (CCA) 模型可以从多式无标签数据中提取信息相关表述。 CCA 模型尽管成功, 如果变量数量超过样本数量, 则可能破碎。 我们建议采用深 Gatet- CACA, 这是一种基于从两种观察到的模式中零散的变量分组学习关联表述的方法。 拟议的程序学习了两种非线性变异, 并同时将输入变量封闭起来, 以识别大多数相关变量的子集。 非线性变异通过培训两个神经网络, 以最大限度地增加基于其产出的共享相关损失来学习。 配对输入变量应用了大约 $\ ell_ 0$ 的正规化术语, 获取 Gated- CAC 。 这个近似于最近提出的一个基于Bernoulli 变量的连续的松动, 以作为大门。 我们用几个合成和真实的例子来证明该方法的有效性。 最显著的是, 方法优于其他线性和非线性的CC模式。