ITR: Representation Learning: Transformations and Kernels for Collections of Tuples

ITR：表示学习：元组集合的转换和内核

• 批准号: 0312690
• 负责人: Tony Jebara
• 金额: $ 24.02万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312690&HistoricalAwards=false
• 关键词: ITR; Representation; Learning; Transformations; Kernels

Statistical machine learning tools permit scientists and engineers to automatically estimate computational models directly from real-world data for making predictions, classifications and inferences. However, before learning can take place, the practitioner needs to know how to properly represent data in a consistent, invariant and well-behavednumerical form for processing by the various techniques. This proposal reduces this burden and facilitates applications of machine learning via novel algorithms that not only model data but also automatically handle invariances and discover appropriaterepresentations of the data.This proposal formalizes a variety of potential transformations and embeds them within a principled learning framework. This makes it possible to handle real scenarios where data transforms, translates, changes nonlinearly and effectively creates many difficulties fortraditional tools. The set of interesting transformations the proposal considers also includes permutations. Handling permutation allows algorithms to learn when each data-point in the dataset is a collection of tuples whose ordering is arbitrary. For example, adigital color image can be represented as a bag of pixels whose ordering is arbitrary. This project then develops the necessary algorithms for invariance to permutations, re-orderings, translations, and many other transformations affecting data in practice.The proposal makes use of and extends state-of-the-art techniques in the machine learning field including Bayesian networks, kernel methods and convex programming. Primary applications include face recognition and surveillance. To recognize human face identity from video, the proposed algorithms compensate for possible transformations as faces translate, deform and rotate in real images. Standardized and challenging face recognition datasets are used for evaluation. The representation learning methods also facilitate machine learning in general, promising potential impact in other applied fields where data undergoes transformations including computational vision, speech, timeseries analysis and bio-informatics.

统计机器学习工具允许科学家和工程师直接从真实世界的数据中自动估计计算模型，以进行预测，分类和推断。然而，在学习之前，从业者需要知道如何正确地表示数据在一个一致的，不变的和良好的数字形式处理的各种技术。该提案减少了这一负担，并通过新的算法促进了机器学习的应用，这些算法不仅可以对数据进行建模，还可以自动处理不变性并发现数据的适当表示。该提案将各种潜在的转换形式化，并将其嵌入原则性学习框架中。这使得它能够处理真实的场景，其中数据转换，转换，非线性变化，并有效地为传统工具创造了许多困难。该提案考虑的一组有趣的转换也包括排列。处理置换允许算法学习数据集中的每个数据点何时是顺序任意的元组的集合。例如，一幅数字彩色图像可以表示为一组任意排列的像素。该项目然后开发必要的算法，以实现对置换，重新排序，翻译和许多其他影响实际数据的转换的不变性。该提案利用并扩展了机器学习领域的最先进技术，包括贝叶斯网络，核方法和凸规划。主要应用包括人脸识别和监控。为了从视频中识别人脸身份，所提出的算法补偿了人脸在真实的图像中平移、变形和旋转时可能发生的变换。标准化和具有挑战性的人脸识别数据集用于评估。表示学习方法也促进了机器学习，在其他应用领域有潜在的影响，其中数据经历了包括计算视觉，语音，时间序列分析和生物信息学在内的转换。