具有低维隐态的多视图数据的最优加权
Optimal Weighting of Multi-View Data with Low Dimensional Hidden States
摘要：
在自然语言处理（NLP）任务中，数据通常具有以下两个属性：第一，数据可以被切割成多视图，这已成功地用于降维的目的。例如，在主题分类中，每篇论文都可以分为标题、正文和参考文献。然而，对于监督学习问题，一些视图比其他视图噪音更小是常见的。第二，未标记的数据很容易获得，而标记的数据相对较少。例如，《纽约时报》最近10年的文章很容易抓取，但要将它们归类为“政治”、“金融”或“体育”则需要人力。因此，在运行监督学习方法之前，噪声较小的特征是首选的。在本文中，我们提出了一种无监督的算法，最佳权重的功能，从不同的意见时，这些意见产生的低维隐藏状态，这发生在广泛使用的模型，如混合高斯模型，隐马尔可夫模型（HMM）和潜在的狄利克雷分配（LDA）。
英文摘要：
In Natural Language Processing (NLP) tasks, data often has the following two properties: First, data can be chopped into multi-views which has been successfully used for dimension reduction purposes. For example, in topic classification, every paper can be chopped into the title, the main text and the references. However, it is common that some of the views are less noisier than other views for supervised learning problems. Second, unlabeled data are easy to obtain while labeled data are relatively rare. For example, articles occurred on New York Times in recent 10 years are easy to grab but having them classified as 'Politics', 'Finance' or 'Sports' need human labor. Hence less noisy features are preferred before running supervised learning methods. In this paper we propose an unsupervised algorithm which optimally weights features from different views when these views are generated from a low dimensional hidden state, which occurs in widely used models like Mixture Gaussian Model, Hidden Markov Model (HMM) and Latent Dirichlet Allocation (LDA).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
