Vector batch SOM algorithms for multi-view dissimilarity data

Mariño, Laura María Palomino; Carvalho, Francisco de A. T. de

doi:10.1016/j.knosys.2022.109994

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…Step 5: Perform singular value decomposition on the feature correlation matrix of the standardized database data [10].…”

Section: Extraction and Fusion Of Duplicate Features In Transfer Datamentioning

confidence: 99%

A method for identifying similar duplicate records in domestic database migration based on an improved LSSVM algorithm

Ding,

Zhang,

et al. 2024

International Conference on Signal Processing and Communication Security (ICSPCS 2024)

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Similar duplicate record recognition can help us effectively remove duplicate and redundant data, improve data quality and consistency. To this end, a domestic database migration similar duplicate record recognition method based on an improved LSSVM algorithm is proposed. Extract domestic database migration data based on crawler structure; Reduce noise and encode the extracted migration data separately to improve data quality; Based on the time cycle, using mutual information as a correlation measure, extract and fuse duplicate features of transfer data; Construct a similar duplicate record recognition model using the LSSVM algorithm, and improve the LSSVM algorithm using the quantum particle swarm optimization algorithm to achieve similar duplicate record recognition in domestic database migration. The experimental results show that the method has high recognition ROC and F1 values, which improves recognition accuracy and efficiency.

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“…Step 5: Perform singular value decomposition on the feature correlation matrix of the standardized database data [10].…”

Section: Extraction and Fusion Of Duplicate Features In Transfer Datamentioning

confidence: 99%