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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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Problems solved by technology

[0003] The purpose of the present invention is to provide a method for abnormal detection of incremental trajectory based on incremental kernel principal component analysis, to solve the problem of high computational complexity of the existing nuclear principal component analysis method

Method used

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  • Incremental track anomaly detection method based on incremental kernel principle component analysis
  • Incremental track anomaly detection method based on incremental kernel principle component analysis
  • Incremental track anomaly detection method based on incremental kernel principle component analysis

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Embodiment 1

[0023] Embodiment 1: Firstly, perform the initialization calculation of the model, and use the traditional Batch KPCA to calculate the initial kernel feature space. Whenever there are M new trajectory data coming, first standardize the M trajectory data; then use Batch KPCA to calculate Add the kernel feature space of the data. Calculate the average reconstruction error of the newly added data and the training data respectively. If the error of the two is greater than the given threshold, perform the subsequent nuclear feature space segmentation-merging method to update the nuclear feature space; then project the updated nuclear feature space , extract the principal components; finally use a class of support vector machines 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 trajectory data...

Embodiment 2

[0037] Embodiment 2: the comparison of the present invention and the incremental trajectory abnormal detection algorithm based on traditional KPCA (Batch KPCA);

[0038] In order to verify the effectiveness of the present invention, a total of 221 trajectories from 7270 trajectory points in the Atlantic hurricane data between 1990 and 2006 were selected as the experimental data set for verification. From figure 2 It can be seen that the execution time of the traditional KPCA-based incremental trajectory anomaly detection method increases rapidly as the sliding data window increases. The execution time of the incremental trajectory anomaly detection algorithm based on kernel feature space segmentation-merging also increases with the increase of the sliding data window, but the magnitude 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-merging takes less computi...

Embodiment 3

[0039] Embodiment 3: An abnormality detection effect diagram of the present invention;

[0040] Select the detection results of the 4th iteration and the 6th iteration of the method of the present invention on the Atlantic hurricane data set, such as image 3 and Figure 4shown. The thicker lines in the figure represent detected abnormal trajectories, and the thinner lines represent normal trajectories. 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 two iterations, it can be seen that with the continuous addition of new trajectories, some new abnormal trajectories are detected, and because the kernel feature space is constantly updated, some earlier anomalous trajectories are ignored.

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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.

Description

technical field [0001] The invention relates to a method for abnormal detection of incremental trajectory, in particular to a method for abnormal detection of incremental trajectory based on incremental kernel principal component analysis. Background technique [0002] Trajectory data contains various features such as geographic location coordinates, speed, direction, etc., and can be regarded as a kind of high-dimensional data. Kernel principal component analysis is a nonlinear principal component analysis trajectory anomaly detection method. The trajectory data is mapped from the original data space to the high-dimensional feature space through nonlinear mapping, and then linear principal component analysis is used in the high-dimensional feature space. feature extraction. However, the computational complexity of kernel principal component analysis in the kernel matrix eigendecomposition is O(N3), which seriously affects the application on large-scale data sets. Introduc...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 张磊樊庆富刘磊军鲍苏宁张国兴
Owner CHINA UNIV OF MINING & TECH
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