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دسته بندی:
تشخیص الگو - Pattern recognition
سال انتشار:
2019
عنوان انگلیسی مقاله:
Locality constrained representation-based K-nearest neighbor classification
ترجمه فارسی عنوان مقاله:
طبقه بندی همسایه نزدیکترین-K مبتنی بر نمایندگی دارای محدودیت محلی
منبع:
Sciencedirect - Elsevier - Knowledge-Based Systems, 167 (2019) 38-52: doi:10:1016/j:knosys:2019:01:016
نویسنده:
Jianping Gou a,∗, Wenmo Qiu a, Zhang Yi b, Xiangjun Shen a, Yongzhao Zhan a, Weihua Ou
چکیده انگلیسی:
K-nearest neighbor rule (KNN) is one of the most widely used methods in pattern recognition. However,
the KNN-based classification performance is severely affected by the sensitivity of the neighborhood
size k and the simple majority voting in the regions of k-neighborhoods, especially in the case of
the small sample size with the existing outliers. To overcome the issues, we propose two locality
constrained representation-based k-nearest neighbor rules with the purpose of further improving the
KNN-based classification performance. The one is the weighted representation-based k-nearest neighbor
rule (WRKNN). In the WRKNN, the test sample is represented as the linear combination of its k-nearest
neighbors from each class, and the localities of k-nearest neighbors per class as the weights constrain their
corresponding representation coefficients. Using the representation coefficients of k-nearest neighbors
per class, the representation-based distance between the test sample and the class-specific k-nearest
neighbors is calculated as the classification decision rule. The other one is the weighted local mean
representation-based k-nearest neighbor rule (WLMRKNN). In the WLMRKNN, k-local mean vectors of
k-nearest neighbors per class are first calculated and then used for representing the test sample. In the
linear combination of the class-specific k-local mean vectors to represent the test sample, the localities of
k-local mean vectors per class are considered as the weights to constrain the representation coefficients
of k-local mean vectors. The representation coefficients are employed to design the classification decision
rule which is the class-specific representation-based distance between the test sample and k-local mean
vectors per class. To demonstrate the effectiveness of the proposed methods, we conduct extensive
experiments on the UCI and UCR data sets and face databases, in comparisons with seven related
competitive KNN-based methods. The experimental results show that the proposed methods perform
better with less sensitiveness to k, especially in the small sample size cases.
Keywords: K-nearest neighbor rule | Local mean vector | Representation-based distance | Pattern recognition
قیمت: رایگان
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