论文标题
在线非负矩阵分解对图像和时间序列数据的应用
Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data
论文作者
论文摘要
在线非负矩阵分解（ONMF）是在线环境中的矩阵分解技术，在线环境中以流方式获取数据，并且每次都会更新矩阵因素。此使因素分析可以同时进行新数据样本的到来。在本文中，我们演示了如何使用在线非负矩阵分解算法从相关数据集的集合中学习关节词典原子。我们根据ONMF算法提出了时间序列数据集的时间词典学习方案。我们在历史温度数据，视频框架和颜色图像的应用程序环境中演示了我们的字典学习技术。
Online nonnegative matrix factorization (ONMF) is a matrix factorization technique in the online setting where data are acquired in a streaming fashion and the matrix factors are updated each time. This enables factor analysis to be performed concurrently with the arrival of new data samples. In this article, we demonstrate how one can use online nonnegative matrix factorization algorithms to learn joint dictionary atoms from an ensemble of correlated data sets. We propose a temporal dictionary learning scheme for time-series data sets, based on ONMF algorithms. We demonstrate our dictionary learning technique in the application contexts of historical temperature data, video frames, and color images.
