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A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction
Zhang, Zhao1; Liu, Cheng-Lin2; Zhao, Ming-Bo3
Source PublicationACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
2015-04-01
Volume6Issue:1
SubtypeArticle
AbstractIn 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.
KeywordAlgorithms Design Experimentation Performance Sparse Projection Low-rank Recovery Similarity Preservation Salient Stroke Feature Extraction HAndwriting Representation And Recognition
WOS HeadingsScience & Technology ; Technology
WOS KeywordFACE RECOGNITION ; PRESERVING PROJECTIONS ; CLASSIFICATION ; ALGORITHM
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS IDWOS:000353638800009
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8116
Collection模式识别国家重点实验室_模式分析与学习
Affiliation1.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
Recommended Citation
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).
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