Spectral clustering algorithm combining local covariance matrix with normalization

Du, Tao; Wen, Guoqiu; Zhi-guo, Cai; Zheng, Wei; Tan, Malong; Li, Yangding

doi:10.1007/s00521-018-3852-z

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…It was necessary to preprocess the original spectra due to the uneven intensity of light sources at different wavelengths and the influence of instrument noise. In this paper, the spectra were processed using ten preprocessing methods, including multiplicative scatter correction (MSC) [ 23 ], standardized normal variate (SNV) [ 24 ], normalization [ 25 ], autoscales [ 26 ], mean centering (MC) [ 27 ], moving average method (MA) [ 28 ], detrend fluctuation analysis (Detrend) [ 29 ], Savitsky–Golay smoothing (SG) [ 30 ], Savitsky–Golay first derivative (SG-FD) [ 31 ], and Savitsky–Golay second derivative (SG-SD) [ 32 ]. To reduce calculation and increase calculation speed, competitive adaptive reweighted sampling (CARS) [ 33 ], principal components analysis (PCA) [ 34 ], and successive projections algorithm (SPA) [ 35 ] are preferable to extract feature wavelengths to reduce the dimensionality.…”

Section: Methodsmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.

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Section: Methodsmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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show abstract

“…It is necessary to preprocess the original spectra due to the uneven intensity of light sources at different wavelengths and the influence of instrument noise. In this paper, the spectra are processed by ten pre-processing methods, including Multiplicative Scatter Correction (MSC) [18], Standardized Normal Variate (SNV) [19], Normalization [20], Autoscales [21], Mean Centering (MC) [22], Moving-Average Method (MA) [23], Detrend Fluctuation Analysis (Detrend) [24], Savitsky-Golay Smoothing (SG) [25], Savitsky-Golay-First Derivative (SG-FD) [26] and Savitsky-Golay-Second Derivative (SG-SD) [27]. to distinguish due to their obvious spoilage and unpleasant smell deterioration, so they will not be discussed in this article.…”

Section: Spectrum Processing Methodsmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Dai¹,

Jiang²,

Lu³

et al. 2020

Preprint

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Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00% and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence due that the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and selecting the incident angle of the light source, and have potential applications in online non-destructive testing.

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“…To fully capture different aspects of the clustering result, we employed the following evaluation metrics, such as accuracy (ACC), normalised mutual information (NMI) and purity (PUR) [14]. report the definitions of the involved evaluation metrics as below.…”

Section: Evaluation Measurementsmentioning

confidence: 99%

“…Where k is clusters number and S i is the number of data points of the i-th class. P i denotes the distribution of correctly partitioned data points in all clusters [14]. First, our proposed methods are sensitive to the setting parameter σ, which controls the similarity between two data points.…”

Section: Evaluation Measurementsmentioning

confidence: 99%

Weighted adjacent matrix for K-means clustering

Zhou

Liu

Zhu

2019

Multimed Tools Appl

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K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.

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Spectral clustering algorithm combining local covariance matrix with normalization

Cited by 10 publications

References 39 publications

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Weighted adjacent matrix for K-means clustering

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