Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as “multi-view data”. Since multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications such as model interpretation and storage reduction. However, it is nontrivial due to the following challenges. First, the data may not fit in memory, because there are too many instances or the feature dimensionality is too large. How to select useful features with limited memory space? Second, the data may come in as streams and concept drift may happen. How to select features from streaming data and handles the concept drift? Third, different views may share some consistent and complementary information. How to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised MultiView Feature Selection, OMVFS, which deals with largescale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via nonnegative matrix factorizatio with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods.

Online Unsupervised Multi-view Feature Selection