2019-2区-Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and Its Application to Hydropower Turbines

Grammar learning has been a bottleneck problem for a long time. In this paper, we propose a method of semantic separator learning, a special case of grammar learning. The method is based on the hypothesis that some classes of words, called semantic separators, split a sentence into several constituents. The semantic separators are represented by words together with their part-of-speech tags and other information so that rich semantic information can be involved. In the method, we first identify t

Video shots are often treated as the basic elements for retrieving information from videos. In recent years, video shotcategorization has received increasing attention, but most of the methods involve a procedure of supervised learning, i.e., training a multi-class predictor (classifier) on the labeled data. In this paper, we study a general framework to unsupervisedly discover video shot categories. The contributions are three-fold in feature, representation, and inference: (1) A new feat

许多行业专家认为，无人监督学习人工智能的下一个前沿，这可能是人工智能研究的关键，即所谓的一般人工智能。由于世界上大多数数据都没有标记，因此无法应用传统的监督学习;这就是无监督学习的用武之地。无监督学习可以应用于未标记的数据集，以发现埋藏在数据深处的有意义的模式，人类几乎不可能发现这些模式。 作者Ankur Patel使用两个简单的，生产就绪的Python框架 - scikit-learn和使用Keras的TensorFlow，提供了有关如何应用无监督学习的实用知识。通过提供实际操作示例和代码，您将识别难以发现的数据模式，获得更深入的业务洞察力，检测异常，执行自动特征工程和选择，以及生成合成数据集。您只需要编程和一些机器学习经验即可开始使用。

作者：Vidal, René, Ma, Yi, Sastry, S.S. 2016年新书。据作者说：研究 unsupervised learning，从一百多年前的PCA讲到压缩感知，知识纵跨上百年。横跨代数几何，数理统计，高维数据处理，优化算法。而应用更涉及科学和工程各个领域，是数据科学的入门基础

Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.

The wake-sleep algorithm for unsupervised neural networks 作者Hinton，提出Helmholtz机和wake-sleep算法