Scalable self-taught deep-embedded learning framework for drug abuse spatial behaviors detection
Document Type
Conference Proceeding
Publication Date
1-1-2019
Abstract
Drug abuse has become an increasingly challenging issue national wide in the United States, while each state has its own legislation regarding such behavior which further stimulates different semantic representations of such behavior over space. To build an accurate and robust classifier to detect such behaviors with spatial variance remains challenging due to the existence of large noise in tweets and limited number of labeled data. Most efforts have utilized humans to label tweets for the base classifier training. The randomness of human labeled data would limit the generalization of base model trained. We propose a deep learning-based centroid-attention framework to consider the spatial variance. We further explore the effect of state-based exemplars on the base model. The performance of the base classifier is thus enhanced.
Identifier
85077772601 (Scopus)
ISBN
[9783030349790]
Publication Title
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
External Full Text Location
https://doi.org/10.1007/978-3-030-34980-6_26
e-ISSN
16113349
ISSN
03029743
First Page
223
Last Page
228
Volume
11917 LNCS
Grant
1416509
Fund Ref
National Science Foundation
Recommended Citation
Liu, Wuji; Ye, Xinyue; Phan, Hai; and Hu, Han, "Scalable self-taught deep-embedded learning framework for drug abuse spatial behaviors detection" (2019). Faculty Publications. 8020.
https://digitalcommons.njit.edu/fac_pubs/8020
