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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Support high-order tensor data description for outlier detection in high-dimensional big sensor data
ترجمه فارسی عنوان مقاله:
توصیف داده های تانسور اولویت بالا برای تشخیص خروجی در داده های حسی با بعد بالا
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, 81 (2018) 177-187: doi:10:1016/j:future:2017:10:013
نویسنده:
Xiaowu Deng a,b,c, Peng Jiang a,*, Xiaoning Peng b,c, Chunqiao Mi b,c
چکیده انگلیسی:
The various high-dimensional sensor data can be collected by wireless sensor networks, video monitoring
systems and multimedia sensor networks, while High-dimensional sensor data is inherently large
scale because each sensor node has spatial attributes and may also be associated with large amounts
of measurement data evolving over time. Detecting outlier in high-dimensional big sensor data is a
challenging task. Most of existing outlier detection methods is based on vector representation. However,
high-dimensional sensor data is naturally described by tensor representations. The vector-based methods
can lead to destroy original structural information and correlation for high-dimensional sensors data,
result in the problem of curse of dimensionality, and some outliers cannot be detected. To solve this
problem, support high-order tensor data description (STDD) and kernel support high-order tensor data
description (KSTDD) are proposed to detect outliers for tensor data. STDD and KSTDD extend support
vector data description from vector space to tensor space. KSTDD maintains the structural information
of data, avoids the problem caused by the vectorization of tensor data, and improves the performance of
outlier detection. Experiments on four sensor datasets show that the proposed method is superior to the
traditional vectorized data analysis method.
Keywords: Big sensor data ، High-dimensional data ، Outlier detection ، CP factorization ، KSTDD
قیمت: رایگان
توضیحات اضافی:
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