A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction

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	A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction
	Zhang, Zhao 1; Liu, Cheng-Lin2 ; Zhao, Ming-Bo 3
发表期刊	ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
	2015-04-01
卷号	6 期号:1
文章类型	Article
摘要	In this article, we consider the problem of simultaneous low-rank recovery and sparse projection. More specifically, a new Robust Principal Component Analysis (RPCA)-based framework called Sparse Projection and Low-Rank Recovery (SPLRR) is proposed for handwriting representation and salient stroke feature extraction. In addition to achieving a low-rank component encoding principal features and identify errors or missing values from a given data matrix as RPCA, SPLRR also learns a similarity-preserving sparse projection for extracting salient stroke features and embedding new inputs for classification. These properties make SPLRR applicable for handwriting recognition and stroke correction and enable online computation. A cosine-similarity-style regularization term is incorporated into the SPLRR formulation for encoding the similarities of local handwriting features. The sparse projection and low-rank recovery are calculated from a convex minimization problem that can be efficiently solved in polynomial time. Besides, the supervised extension of SPLRR is also elaborated. The effectiveness of our SPLRR is examined by extensive handwritten digital repairing, stroke correction, and recognition based on benchmark problems. Compared with other related techniques, SPLRR delivers strong generalization capability and state-of-the-art performance for handwriting representation and recognition.
关键词	Algorithms Design Experimentation Performance Sparse Projection Low-rank Recovery Similarity Preservation Salient Stroke Feature Extraction HAndwriting Representation And Recognition
WOS标题词	Science & Technology ; Technology
关键词[WOS]	FACE RECOGNITION ; PRESERVING PROJECTIONS ; CLASSIFICATION ; ALGORITHM
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000353638800009
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8116
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	1.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zhao,Liu, Cheng-Lin,Zhao, Ming-Bo. A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2015,6(1).
APA	Zhang, Zhao,Liu, Cheng-Lin,&Zhao, Ming-Bo.(2015).A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,6(1).
MLA	Zhang, Zhao,et al."A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 6.1(2015).