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
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[0023] Example 1: First perform the initialization calculation of the model, and use the traditional Batch KPCA to perform the initial kernel feature space calculation. Whenever there are M new trajectory data coming, the M trajectory data are first standardized; then the Batch KPCA calculation is used The nuclear feature space of the newly added data. Calculate the average reconstruction error of the newly added data and the training data respectively. If the errors of the two are greater than the given threshold, perform the subsequent kernel feature space segmentation-merging method to update the kernel feature space; then project the updated kernel feature space , Extract the principal components; finally use a type of support vector machine for unsupervised learning and anomaly detection.
[0024] See figure 1 As shown, an incremental trajectory anomaly detection method based on incremental kernel principal component analysis includes the following steps:
[0025] (1) Initial t
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[0037] Embodiment 2: Comparison between the present invention and the incremental trajectory anomaly detection algorithm based on traditional KPCA (Batch KPCA);
[0038] In order to verify the effectiveness of the present invention, a total of 221 trajectories with 7,270 trajectory points between 1990 and 2006 in the Atlantic hurricane data were selected as the experimental data set for verification. From figure 2 It can be seen that the execution time of the incremental trajectory anomaly detection method based on the traditional KPCA is increasing rapidly with the increase of the sliding data window. The execution time of the incremental trajectory anomaly detection algorithm based on kernel feature space segmentation and merging will also increase with the increase of the sliding data window, but the amplitude is not large. And in the case of the same sliding data window size, the incremental trajectory anomaly detection algorithm based on kernel feature space segmentation and m
Example Embodiment
[0039] Embodiment 3: The abnormality detection effect diagram of the present invention;
[0040] Select the detection results of the fourth iteration and the sixth iteration of the method of the present invention on the Atlantic hurricane data set, such as image 3 with Figure 4 Shown. The thicker line in the figure represents the detected abnormal trajectory, and the thinner line represents the normal trajectory. It can be seen from the figure that the anomaly detection effect is very good, and many trajectories with abnormal behavior have been detected. And from the results of the two iterations, it can be seen that with the continuous addition of new trajectories, some new abnormal trajectories are detected, and because the nuclear feature space is constantly updated, some previous abnormal trajectories are ignored.
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