Incremental track anomaly detection method based on incremental kernel principle component analysis
A nuclear principal component analysis and anomaly detection technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problem of high computational complexity, and achieve the effect of improving efficiency and reducing computational complexity
Active Publication Date: 2016-10-12
CHINA UNIV OF MINING & TECH
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Abstract
The invention provides an incremental track anomaly detection method based on incremental kernel principle component analysis, and belongs to the field of an incremental track anomaly detection method. The method comprises the following steps: to begin with, carrying out model initialization calculation, carrying out initial kernel feature space calculation through conventional Batch KPCA, and when M newly-increased track data comes, carrying out standardization on the M track data first; then, calculating kernel feature space of the newly-increased data through Batch KPCA; calculating average reconstruction error of the newly-increased data and training data, and if the error of the two is larger than a preset threshold value, using a follow-up kernel feature space division-merging method to update kernel feature space; then, carrying out projection on the updated kernel feature space and extracting a principal component; and finally, carrying out unsupervised learning and anomaly detection by utilizing a support vector machine. The advantages are that the method is superior to a conventional kernel principle component analysis method; computing complexity is reduced; and track anomaly detection efficiency is improved.
Application Domain
Character and pattern recognition
Technology Topic
Kernel principal component analysisCharacteristic space +7
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