NDLI: f-Fractional Bit Minwise Hashing for Large-Scale Learning

Content Provider	IEEE Xplore Digital Library
Author	Jingjing Tang Yingjie Tian
Copyright Year	2015
Description	Author affiliation: Sch. of Math. Sci., Univ. of Chinese Acad. of Sci., Beijing, China (Jingjing Tang) \|\| Res. Center on Fictitious Econ. & Data Sci., Key Lab. of Big Data Min. & Knowledge Manage., Beijing, China (Yingjie Tian)
Abstract	Recently, document similarity detection technology captures a host of researchers' attention. In this paper, we propose to integrate linear SVM with f-fractional bit minwise hashing to make a wide range of choices for accuracy and storage space requirements. According to the derived properties of f-fractional bit minwise hashing, we obtained the optimal combination of fractional bit with the minimum estimator of variances and ultimately applied it to the process of integration. The innovation of this algorithm is the continuous selectivity of bit instead of the discrete integer value, which not only improves the theoretical system of b-bit minwise hashing SVM algorithm, but also satisfies the various needs of accuracy and storage space in the practical system. Due to the nonlinear of the resemblance matrix considered as kernel matrix, it can not be used in linear SVM training directly. However, in the theoretical analysis, we provide the proof of positive definiteness for resemblance matrix generated by f-fractional bit minwise hashing scheme, which is a logical and feasible basis for the integration. Meanwhile, experimental results on publicly available large-scale datasets validate the effectiveness of this algorithm.
Sponsorship	IEEE Comput. Soc.
Starting Page	60
Ending Page	63
File Size	210364
Page Count	4
File Format	PDF
e-ISBN	9781467396189
DOI	10.1109/WI-IAT.2015.104
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Radio frequency minwise hashing similarity estimation Optimized production technology f-fractional bit Matrix decomposition large-scale Matrix converters Kernel linear SVM
Content Type	Text
Resource Type	Article

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