Multi-view learning with Gaussian Process Latent Variable Models

使用高斯过程潜变量模型进行多视图学习

• 批准号: 1806689
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1806689
• 关键词: Multi; view; learning; Gaussian; Process

The aim of this research project is to develop methodology for data-efficient and interpretable modeling frommultiple views. The focus will be on nonparametric Bayesian methods such as models based on Gaussianprocesses. The projects goals are to develop machine learning that will be applicable to a large range ofdifferent scenarios. We will initially focus on applications which use motion capture data. Our motivation forthis is twofold. Human motion is something which is readily interpretable by humans, making it easy to attach"meaning" to results. This means that we can evaluate our first goal, creating interpretable models. Secondly,we wish to be data efficient. Motion capture data is expensive to collect and requires specialist equipment.Therefore we will have to learn from small amounts of data which requires us to use it in its most efficientform. This also leads to the multi-view motivation, using motion capture data as an example, given that we haveseveral people performing the same action we will see each of these as views of the same underlying conceptdirectly expanding our dataset compared to if we had to learn from only one person.Even though motivated by motion capture data multi-view learning is applicable to a large range of scenarios.Virtually everything can be seen from more than one perspective. Such as a car driving on the street seen bytwo people from different angles; or just by the two eyes of a single person. Another perspective of the car isthe sound of it, or even the smell of it. A face from the perspectives of being ten different individuals, or intwenty different lighting conditions. Perspectives are not limited to being of physical objects; it can also be e.g.the action of walking from the perspectives of being carried out by different individuals or groups of people. Ora song from the perspectives of being performed by different musicians. Multi-view learning is focused onexploiting these connections in the data to uncover latent concepts that explains each of the views in a jointmanner. This allows us to understand in what aspects the different views are similar and where they differ; aswell as conducting inference between them. A central aspect of any learning systems is to be able to interrogatea model. What has been learnt? How likely is the model? What is the certainty of predictions? Besidesunderstanding being the essence of many applications, such as analysis or diagnosis, it is vital for trusting theresult of learning, the assumptions made, predictions as well as for guidance and comparison useful for furtherdevelopment. Over the last decade we have seen machine learning successfully applied to a large range of newdomains. Many of its successes comes from availability of large data sets. In many ways, large amounts of datahave reduced the demands of learning systems as they can be allowed to be less abstract. However, for manyapplications large datasets are not (and will most likely not be) available which means we need to use theinformation available in the data as efficiently as possible. Therefore we will work on data efficient models thatuse principled uncertainty propagation in order to reduce the need for large data sets.

本研究项目的目的是开发从多个视图进行数据高效和可解释建模的方法。重点将放在非参数贝叶斯方法上，例如基于高斯过程的模型。该项目的目标是开发适用于各种不同场景的机器学习。我们首先将重点放在使用运动捕捉数据的应用程序。我们这样做的动机是双重的。人类的运动很容易被人类理解，因此很容易将“意义”附加到结果上。这意味着我们可以评估我们的第一个目标，创建可解释的模型。其次，我们希望数据高效。运动捕捉数据的收集成本很高，而且需要专门的设备。因此，我们必须从少量数据中学习，这需要我们以最有效的形式使用它。这也导致了多视角的动机，以运动捕捉数据为例，考虑到我们有几个人执行相同的动作，我们将把这些人中的每一个都视为相同基本概念的视图，与如果我们只能从一个人那里学习相比，直接扩展我们的数据集。尽管动机是运动捕捉数据，但多视角学习适用于广泛的场景。实际上，任何事情都可以从多个角度来看。比如一辆车在街上行驶，两个人从不同的角度看到；或者只由一个人的两只眼睛看到。汽车的另一个视角是它的声音，甚至是它的气味。从十个不同的个体或二十个不同的光照条件的角度来看一张脸。视角并不局限于物理物体的存在；它也可以是从不同的个人或群体进行的视角行走的行为。从不同音乐家表演的角度看ORA歌曲。多视图学习的重点是利用数据中的这些联系来发现潜在的概念，这些概念以一种联合的方式解释每一种视图。这使我们能够了解不同观点在哪些方面相似，在哪里不同，以及在它们之间进行推理。任何学习系统的一个核心方面是能够询问模型。我们学到了什么？这种模式的可能性有多大？预测的确定性是什么？除了理解是许多应用的本质之外，例如分析或诊断，它对于信任学习的结果、所作的假设、预测以及对进一步发展有用的指导和比较是至关重要的。在过去的十年里，我们看到机器学习成功地应用于许多新的领域。它的许多成功都来自于大型数据集的可用性。在许多方面，大量的数据减少了对学习系统的要求，因为它们可以被允许不那么抽象。然而，对于许多应用程序来说，大型数据集是不可用的(也很可能是不可用的)，这意味着我们需要尽可能高效地使用数据中可用的信息。因此，我们将致力于使用原则性不确定性传播的数据高效模型，以减少对大型数据集的需求。