In this work, we propose the joint linked component analysis (joint\_LCA) for
multiview data. Unlike classic methods which extract the shared components in a
sequential manner, the objective of joint\_LCA is to identify the view-specific
loading matrices and the rank of the common latent subspace simultaneously. We
formulate a matrix decomposition model where a joint structure and an
individual structure are present in each data view, which enables us to arrive
at a clean svd representation for the cross covariance between any pair of data
views. An objective function with a novel penalty term is then proposed to
achieve simultaneous estimation and rank selection. In addition, a refitting
procedure is employed as a remedy to reduce the shrinkage bias caused by the
penalization.

提出了联合链接分量分析（joint_LCA）方法，用于多视图数据，通过同时识别视图特定的加载矩阵和公共潜在子空间的秩，来提取共享组件。采用矩阵分解模型，通过一个联合结构和一个个体结构来表示每个数据视图，以便得到任意数据视图之间的交叉协方差的干净奇异值分解表示。同时提出了带有新型惩罚项的目标函数来实现估计和秩选择，并采用修复程序来减小惩罚造成的收缩偏差。

多视角数据的联合链式分析

Joint Linked Component Analysis for Multiview Data

Generative models that learn disentangled representations for different
factors of variation in an image can be very useful for targeted data
augmentation. By sampling from the disentangled latent subspace of interest, we
can efficiently generate new data necessary for a particular task. Learning
disentangled representations is a challenging problem, especially when certain
factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary
subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures,
we use cycle-consistency in a variational auto-encoder framework. Our
non-adversarial approach is in contrast with the recent works that combine
adversarial training with auto-encoders to disentangle representations. We show
compelling results of disentangled latent subspaces on three datasets and
compare with recent works that leverage adversarial training.

本文提出了一种使用弱监督来将潜在空间分离成两个互补子空间的新架构，使用变分自编码器中的循环一致性，获得鼓舞人心的结果，并与利用对抗训练的最近作品进行了比较。