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Reductive and effective discriminative information-based nonparallel support vector machine. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02874-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data. REMOTE SENSING 2021. [DOI: 10.3390/rs13040750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.
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Gu Y, Yang J. Multi-level magnification correlation hashing for scalable histopathological image retrieval. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.050] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Abin AA, Bashiri MA, Beigy H. Learning a metric when clustering data points in the presence of constraints. ADV DATA ANAL CLASSI 2019. [DOI: 10.1007/s11634-019-00359-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Li Y, Wang Y, Bi C, Jiang X. Revisiting transductive support vector machines with margin distribution embedding. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.04.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Peng Y, Li L, Liu S, Lei T, Wu J. A New Virtual Samples-Based CRC Method for Face Recognition. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9721-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Tu E, Zhang Y, Zhu L, Yang J, Kasabov N. A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.07.016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Fu H, Niu Z, Zhang C, Yu H, Ma J, Chen J, Chen Y, Liu J. ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.05.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Song X, Liu Z, Yang X, Yang J, Qi Y. Extended semi-supervised fuzzy learning method for nonlinear outliers via pattern discovery. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.12.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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