Titlebook: Linking and Mining Heterogeneous and Multi-view Data; Deepak P,Anna Jurek-Loughrey Book 2019 Springer Nature Switzerland AG 2019 Dimension

指耕作

Multi-View Data Completion,-view learning methods commonly assume that full feature matrices or kernel matrices for all views are available. However, in partial data analytics, it is common that information from some sources is not available or missing for some data-points. Such lack of information can be categorized into two

違抗

Multi-View Clustering,ly leads to datasets comprising the same set of data objects represented in different forms, called multi-view data. Among the most fundamental tasks in unsupervised learning is that of clustering, the task of grouping data objects into groups of related objects. Multi-view clustering (MVC) is a flo

極為憤怒

Obsessed

A Review of Unsupervised and Semi-supervised Blocking Methods for Record Linkage,oss varied data sources. To reduce the computational complexity associated with record comparisons, a task referred to as blocking is commonly performed prior to the linkage process. The blocking task involves partitioning records into blocks of records and treating records from different blocks as

MAUVE

Traffic Sensing and Assessing in Digital Transportation Systems,ehicles themselves, the main transportation problems can be alleviated and road safety improved along with an increase in economic productivity. This new cooperative environment integrates networking, electronic, and computing technologies, will enable safer roads, and achieve more efficient mobilit

machination

寬大

Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods,also called views—are available. Each view incorporates various information on the examples, and in particular, depending on the task at hand, each view might be better at recognizing only a subset of the classes. Establishing a sort of cooperation between the views is needed for all the classes to

殘忍

Sad570

Leveraging Heterogeneous Data for Fake News Detection,m a deluge, in which it is hard to believe all the pieces of information since it appears to be very realistic. In this context, characterizing and recognizing misinformation, especially, fake news, is a highly recommended computational task. News fabrication mostly happens through the textual and v

懸崖

General Framework for Multi-View Metric Learning,n vector-valued kernel spaces, as a way to capture multimodal structure of the data. We formulate a general convex optimization problem in this context to jointly learn the metric and the classifier or regressor in kernel feature spaces. The formulated multi-view metric learning (MVML) can be applie