For a learning task, data can usually be collected from different sources or
be represented from multiple views. For example, laboratory results from
different medical examinations are available for disease diagnosis, and each of
them can only reflect the health state of a person from a particular
aspect/view. Therefore, different views provide complementary information for
learning tasks. An effective integration of the multi-view information is
expected to facilitate the learning performance. In this paper, we propose a
general predictor, named multi-view machines (MVMs), that can effectively
include all the possible interactions between features from multiple views. A
joint factorization is embedded for the full-order interaction parameters which
allows parameter estimation under sparsity. Moreover, MVMs can work in
conjunction with different loss functions for a variety of machine learning
tasks. A stochastic gradient descent method is presented to learn the MVM
model. We further illustrate the advantages of MVMs through comparison with
other methods for multi-view classification, including support vector machines
(SVMs), support tensor machines (STMs) and factorization machines (FMs).

本文提出了一种名为多视角机器（MVM）的通用预测器，其可以有效地包括多个视图中特征之间的所有可能交互，并嵌入联合分解以实现参数稀疏估计。通过与其他方法进行比较，我们进一步说明了 MVM 的优势，包括支持向量机（SVM）、支持张量机（STMs）和因子机（FMs）等多视角分类任务。同时我们还提出了一个用于学习 MVM 模型的随机梯度下降方法。