Crowdsourcing has been used to collect data at scale in numerous fields.
Triplet similarity comparison is a type of crowdsourcing task, in which crowd
workers are asked the question ``among three given objects, which two are more
similar?'', which is relatively easy for humans to answer. However, the
comparison can be sometimes based on multiple views, i.e., different
independent attributes such as color and shape. Each view may lead to different
results for the same three objects. Although an algorithm was proposed in prior
work to produce multiview embeddings, it involves at least two problems: (1)
the existing algorithm cannot independently predict multiview embeddings for a
new sample, and (2) different people may prefer different views. In this study,
we propose an end-to-end inductive deep learning framework to solve the
multiview representation learning problem. The results show that our proposed
method can obtain multiview embeddings of any object, in which each view
corresponds to an independent attribute of the object. We collected two
datasets from a crowdsourcing platform to experimentally investigate the
performance of our proposed approach compared to conventional baseline methods.

本研究提出了一种端到端归纳深度学习框架，解决了多视图表示学习问题，并在众包平台上收集了两个数据集以实验比较我们的方法与传统基线方法的性能。

来自众包三元组比较的多视角表示学习

Multiview Representation Learning from Crowdsourced Triplet Comparisons

Multiview representation learning of data can help construct coherent and
contextualized users' representations on social media. This paper suggests a
joint embedding model, incorporating users' social and textual information to
learn contextualized user representations used for understanding their
lifestyle choices. We apply our model to tweets related to two lifestyle
activities, `Yoga' and `Keto diet' and use it to analyze users' activity type
and motivation. We explain the data collection and annotation process in detail
and provide an in-depth analysis of users from different classes based on their
Twitter content. Our experiments show that our model results in performance
improvements in both domains.

通过多视角表示学习，结合用户的社交和文本信息，构建相关用户的上下文表示，以理解他们的生活方式选择，该文提出了一种联合嵌入模型并将其应用于推文分析。在实验中，模型在 Yoga 和 Keto 饮食领域中均取得了较好的表现。

利用联合嵌入模型分析 Twitter 用户的生活方式选择

Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

Multiview representation learning is very popular for latent factor analysis.
It naturally arises in many data analysis, machine learning, and information
retrieval applications to model dependent structures among multiple data
sources. For computational convenience, existing approaches usually formulate
the multiview representation learning as convex optimization problems, where
global optima can be obtained by certain algorithms in polynomial time.
However, many pieces of evidence have corroborated that heuristic nonconvex
approaches also have good empirical computational performance and convergence
to the global optima, although there is a lack of theoretical justification.
Such a gap between theory and practice motivates us to study a nonconvex
formulation for multiview representation learning, which can be efficiently
solved by a simple stochastic gradient descent (SGD) algorithm. We first
illustrate the geometry of the nonconvex formulation; Then, we establish
asymptotic global rates of convergence to the global optima by diffusion
approximations. Numerical experiments are provided to support our theory.

非凸多视角表示学习问题最近在机器学习领域中备受关注，本文提出一种由简单随机梯度下降算法有效求解的非凸公式，并且通过扩散近似建立起渐近全局收敛速率的理论证明。

放弃凸性以获得更高效可伸缩的在线多视角学习

Dropping Convexity for More Efficient and Scalable Online Multiview  Learning

We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a
method for learning nonlinear transformations of arbitrarily many views of
data, such that the resulting transformations are maximally informative of each
other. While methods for nonlinear two-view representation learning (Deep CCA,
(Andrew et al., 2013)) and linear many-view representation learning
(Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview
representation learning technique that combines the flexibility of nonlinear
(deep) representation learning with the statistical power of incorporating
information from many independent sources, or views. We present the DGCCA
formulation as well as an efficient stochastic optimization algorithm for
solving it. We learn DGCCA representations on two distinct datasets for three
downstream tasks: phonetic transcription from acoustic and articulatory
measurements, and recommending hashtags and friends on a dataset of Twitter
users. We find that DGCCA representations soundly beat existing methods at
phonetic transcription and hashtag recommendation, and in general perform no
worse than standard linear many-view techniques.

提出了 Deep Generalized Canonical Correlation Analysis (DGCCA) 方法，通过学习非线性变换最大化信息关联，结合非线性（深度）表示学习的灵活性与多个独立来源视图的信息统计力量，通过在两个不同数据集上的三个下游任务训练，成功击败了已有的标准线性多视图技术，在语音转录和 Twitter 用户的 hashtag 推荐等方面表现更好。