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Exploring the Power of Kernel in Feature Representation for Object Categorization
Weiqiang Ren; Yinan Yu; Junge Zhang; Kaiqi Huang
2013
会议名称International Conference on Neural Information Processing
会议录名称International Conference on Neural Information Processing
页码541-548
会议日期November  2013
会议地点Daegu, Korea
摘要Learning robust and invariant feature representations is always a crucial task in visual recognition and analysis. Mean square error (MSE) has been used in many feature encoding methods as a feature reconstruction criterion. However, due to the non-Gaussian noises and non-linearity structures in natural images, second order statistics like MSE are usually not sufficient to capture these information from image data. In this paper, motivated by the information-theoretic learning framework and kernel machine learning, we adopt a similarity measure called correntropy in the auto-encoder model to tackle this problem. The proposed maximum correntropy auto-encoder (MCAE) learns more robust and discriminative representations than MSE based model by performing computation in an infinite dimensional kernel space. Moreover, we further exploit the power of kernel by learning a kernel embedding neural network which explicitly maps data from Euclidean space to an approximated kernel space. Experimental results on standard object categorization datasets show the effectiveness of kernel learning in feature representation for visual recognition task.
关键词Auto Encoder   maximum Correntropy   explicit Kernel Embedding
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12686
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Weiqiang Ren,Yinan Yu,Junge Zhang,et al. Exploring the Power of Kernel in Feature Representation for Object Categorization[C],2013:541-548.
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