[1]仝伯兵,王士同,梅向东.稀疏条件下的两层分类算法[J].智能系统学报,2015,10(01):27-36.[doi:10.3969/j.issn.1673-4785.201407019]
 TONG Bobing,WANG Shitong,MEI Xiangdong.Sparsity-inspired two-level classification algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(01):27-36.[doi:10.3969/j.issn.1673-4785.201407019]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年01期
页码:
27-36
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Sparsity-inspired two-level classification algorithm
作者:
仝伯兵1 王士同1 梅向东2
1. 江南大学 数字媒体学院, 江苏 无锡 214122;
2. 赞奇科技发展有限公司, 江苏 常州 213000
Author(s):
TONG Bobing1 WANG Shitong1 MEI Xiangdong2
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. Zanqi Science Technology Development Co., Ltd, Changzhou 213000, China
关键词:
稀疏贝叶斯两层分类距离学习视频烟雾检测最近邻算法有限样本泛化性时间效率
Keywords:
parse Bayesiantwo-level classificationdistance learningvideo smoke detectionKNNfinite samplesgeneralizationtime efficiency
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201407019
文献标志码:
A
摘要:
在有限样本下距离量的选择对最近邻算法(K-nearest neighbor,KNN)算法有重要影响。针对以前距离量学习泛化性不强以及时间效率不高的问题,提出了一种稀疏条件下的两层分类算法(sparsity-inspired two-level classification algorithm,STLCA)。该算法分为高低2层,在低层使用欧氏距离确定一个未标记的样本局部子空间;在高层,用稀疏贝叶斯在子空间进行信息提取。由于其稀疏性,在噪声情况下有很好的稳定性,可泛化性强,且时间效率高。通过在噪声数据以及在视频烟雾检测中的应用表明,STLCA算法能取得更好的效果。
Abstract:
The selection of distance greatly affects KNN algorithm as it relates to finite samples due to weak generalization and low time efficiency in the previous learning of distance. In this paper, a new sparsity-inspired two-level classification algorithm (STLCA) is proposed. This proposed algorithm is divided into two levels: high and low. It uses Euclidean distance at the low-level to determine an unlabeled sample local subspace and at the high level it uses sparse Bayesian to extract information from subspace. Due to the sparsity in noise conditions, STLCA can have good stability, strong generalization and high time efficiency. The results showed that the STLCA algorithm can achieve better results through the application in noise data and video smoke detection.

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备注/Memo

备注/Memo:
收稿日期:2014-7-14;改回日期:。
基金项目:国家自然科学基金资助项目(61170122,61272210);江苏省自然科学基金资助项目(BK2011417);江苏省“333”工程基金资助项目(BRA2011142).
作者简介:仝伯兵,男,1989生,硕士研究生,主要研究方向为人工智能与模式识别、数字图像处理;王士同,男,1964生,教授,博士生导师,主要研究方向为人工智能、模式识别和生物信息;梅向东,男,1966生,高级工程师,主要研究方向为多媒体及计算机应用。
通讯作者:仝伯兵.E-mail:tongbobing@163.com.
更新日期/Last Update: 2015-06-16