CAREER: Supervised Learning for Incomplete and Uncertain Data

职业：不完整和不确定数据的监督学习

• 批准号: 1723891
• 负责人: Alina Zare
• 金额: $ 29.54万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723891&HistoricalAwards=false
• 关键词: CAREER; Supervised; Learning; Incomplete; Uncertain

This CAREER project will advance the state of the art in supervised machine learning to allow for incomplete, uncertain and unspecific label information. Supervised machine learning algorithms produce desired outputs for given input data by learning from example training data. The methods generally rely on completely and accurately labeled training data to drive the learning algorithm. However, many applications are plagued with labels that are incomplete, uncertain, and unspecific (lack precision). Current techniques do not adequately handle such data.For example, analysis of satellite imagery to identify the content of each pixel is often conducted by coupling unsupervised learning methods (that do not rely on labeled training data) with manual exploration. This is time-consuming, error-prone, and expensive. Imagine, instead, easy-to-use tools that could understand the content of each pixel in satellite imagery. Extremely large amounts of road map data (for example from Google Maps or OpenStreetMap) and social media information (for example geo-tagged photographs, video clips, and social networking posts) are continually collected and stored. These data could be used as sparsely-labeled training data (with varying degrees of specificity and uncertainty) to guide understanding of satellite imagery.Although the data is available, algorithms have yet to be developed to combine these data sources and identify the content of pixels in satellite images. This work will advance this and other potential applications of machine learning where incomplete, uncertain and unspecific labels in training data challenge the development of effective machine learning algorithms. This CAREER project will achieve these advances through the following research objectives:(1) Investigate and develop a mathematical framework and associated algorithms for Multiple Instance Function Learning that addresses linear and non-linear classification and regression problems with varying levels and types of sparsity, uncertainty, and specificity in training labels.(2) Study and apply the proposed framework and algorithms towards the fusion of satellite imagery, road map data and social media for global scene understanding. This research will be conducted in conjunction with integrated education and outreach activities. In particular, an interactive web application will be developed to provide an avenue for introducing concepts from machine learning and remote sensing to the public for dissemination and outreach. This interactive web application will also be used, along with additional hands-on activities, to introduce high school students to machine learning and remote sensing concepts during an annual summer engineering camp held at the University of Missouri in Columbia, MO. Paired with the web application will be a research website in which data, code, publications and presentations will be shared with the research community. Furthermore, undergraduate and graduate research assistants will be trained in the areas of machine learning and remote sensing. Finally, relevant research topics will be introduced in the PI's undergraduate and graduate courses.

这个CAREER项目将推进监督机器学习的最新技术，以允许不完整，不确定和不特定的标签信息。 受监督的机器学习算法通过从示例训练数据中学习来为给定的输入数据产生期望的输出。 这些方法通常依赖于完整和准确标记的训练数据来驱动学习算法。然而，许多应用程序都受到不完整、不确定和非特异性（缺乏精确性）标签的困扰。 例如，分析卫星图像以识别每个像素的内容通常是通过将无监督学习方法（不依赖于标记的训练数据）与手动探索相结合来进行的。 这是耗时的、容易出错的并且昂贵的。 想象一下，相反，易于使用的工具可以理解卫星图像中每个像素的内容。 大量的道路地图数据（例如来自谷歌地图或OpenStreetMap）和社交媒体信息（例如地理标记的照片，视频剪辑和社交网络帖子）被不断收集和存储。 这些数据可用作稀疏标记的训练数据（具有不同程度的具体性和不确定性），以指导对卫星图像的理解，虽然这些数据是可用的，但尚未开发出算法来联合收割机这些数据源并确定卫星图像中像素的内容。 这项工作将推进机器学习的这一应用和其他潜在应用，其中训练数据中不完整、不确定和不特定的标签挑战了有效机器学习算法的开发。 该职业项目将通过以下研究目标实现这些进步：（1）调查并开发多实例函数学习的数学框架和相关算法，以解决具有不同水平和类型的稀疏性、不确定性和特殊性的线性和非线性分类和回归问题。训练标签中的特异性。(2)研究并应用所提出的框架和算法，将卫星图像、道路图数据和社交媒体融合在一起，以实现对全球场景的理解。这项研究将与综合教育和外联活动一起进行。 特别是，将开发一个交互式网络应用程序，为向公众介绍机器学习和遥感概念提供一个渠道，以便进行传播和推广。在密苏里州哥伦比亚的密苏里州大学举行的年度工程夏令营期间，还将使用这一交互式网络应用程序，沿着其他实践活动，向高中生介绍机器学习和遥感概念。 与网络应用程序配对的将是一个研究网站，其中数据，代码，出版物和演示文稿将与研究界共享。此外，本科生和研究生研究助理将接受机器学习和遥感领域的培训。最后，相关的研究课题将在PI的本科和研究生课程中介绍。