Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2017

Abstract

Streaming classification with emerging new class is an important problem of great research challenge and practical value. In many real applications, the task often needs to handle large matrices issues such as textual data in the bag-of-words model and large-scale image analysis. However, the methodologies and approaches adopted by the existing solutions, most of which involve massive distance calculation, have so far fallen short of successfully addressing a real-time requested task. In this paper, the proposed method dynamically maintains two low-dimensional matrix sketches to 1) detect emerging new classes; 2) classify known classes; and 3) update the model in the data stream. The update efficiency is superior to the existing methods. The empirical evaluation shows the proposed method not only receives the comparable performance but also strengthens modelling on large-scale data sets.

Keywords

Bag-of-words models, Distance calculation, Empirical evaluations, Large scale data sets, artificial intelligence

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17): San Francisco, CA, February 4-9

First Page

2373

Last Page

2379

Publisher

AAAI

City or Country

Menlo Park, CA

Copyright Owner and License

Publisher

Additional URL

https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14514

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