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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Qinghua Yu Hongtao Deng Donghua |
| Copyright Year | 2015 |
| Description | Author affiliation: Mech. & Electr. Eng. Dept., Xi'an Technol. Univ., Xi'an, China (Wang Qinghua; Yu Hongtao) || China Pet. Pipeline Eng. Co., Ltd., Langfang, China (Deng Donghua) |
| Abstract | A method of image compression or dimensional reduction is proposed to avoid the dimension disaster in machine learning and the bottleneck in extracting and selecting sensitive characteristics, which can make it easier for automatic image recognition. Sparse non-negative matrix factorization (Sparse NMF) and the Probabilistic Neural Networks (PNN) are used to recognize the time-frequency images automatically for diesel valve trains. The comparison of the results of sparse NMF with the results of pane division have found that the former has higher correct recognition rate, which indicates that sparse NMF is an effective method for dimension reduction while preserving or even enhancing the information content. Thus the computation complexity is greatly reduced and the correct recognition rate is effectively improved. |
| Starting Page | 2408 |
| Ending Page | 2413 |
| File Size | 1044954 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479970971 |
| e-ISBN | 9781479970988 |
| DOI | 10.1109/ICMA.2015.7237864 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-02 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Time-frequency analysis Image recognition Valves Matrix decomposition Training Feature extraction Acceleration PNN Time-frequency image Automatic recognition Sparse NMF |
| Content Type | Text |
| Resource Type | Article |
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