A near-infrared anomaly spectral identification method based on knn
A technology for near-infrared spectroscopy and spectral identification, which is applied in the field of near-infrared anomaly spectral identification based on KNN, and can solve problems affecting the performance of near-infrared quantitative analysis models.
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Embodiment 1
[0026] Example 1: KNN abnormal spectrum identification method based on Euclidean distance metric
[0027] Euclidean distance KNN: Based on the K-nearest neighbor method of Euclidean distance, the Euclidean distance between the sample and its K-nearest neighbor sample is used as an anomaly measure;
[0028] Euclidean distance is the most commonly used distance measure and similarity measure between samples. The calculation of Euclidean distance is simple and fast, and it has advantages in calculation speed and algorithm implementation. It often becomes the preferred option in the case of similar performance. The outlier identification method based on Euclidean distance KNN is given below. On the one hand, it examines and verifies its ability to identify abnormal spectral data.
[0029] input: a The training set spectral data matrix X, n is the spectral sample, and p is the number of measured wavelength points.
[0030] 1) Select Euclidean distance as the similarity measure ...
Embodiment 2
[0038] Example 2: KNN abnormal spectrum identification method based on principal component space distance metric
[0039]Based on the PC-KNN method of principal component normalization space, the spectral data is subjected to principal component analysis, and on the basis of the data obtained after all principal components (PC) are standardized, the K-nearest neighbor method based on Euclidean distance is used.
[0040] Based on the similarity measure between samples based on Euclidean distance, the underlying assumption is that the variance of the sample distribution in each direction is basically equal. However, in practical applications, this condition may not necessarily be satisfied. The similarity measure between samples based on the Mahalanobis distance has no assumptions and requirements for the variance in each direction. However, the calculation of Mahalanobis distance is not suitable for occasions where small samples or data contain a large number of cross-correlat...
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