A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

Liao, Yuwei; Kakde, Deovrat; Chaudhuri, Arin; Jiang, Hansi; Sadek, Carol; Kong, Seung-Hyun

doi:10.1117/12.2314964

Cited by 6 publications

(6 citation statements)

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“…In this work, two automatic bandwidth parameter selection approaches [viz. var criteria (VC) and modified mean criteria (MMC)] are considered to select an appropriate value of c (Kakde et al , 2018). Section 4 provides a comparative performance analysis of these two automatic kernel parameter selection approaches.…”

Section: Proposed Occ-svm Single Multivariate Chart Approach For Simu...mentioning

confidence: 99%

“…Thus, an unsupervised classifier seems necessary to monitor the multivariate process quality in a product manufacturing environment.The selection of appropriate tuned intrinsic parameters for a nonparametric control approach is another challenge generally faced by researchers (Salehi et al , 2012). Heuristic choice of parameters can drastically degrade the performance of a classifier (Kakde et al , 2018). Thus, an appropriate parameter selection strategy is considered critical for a nonparametric or ML-based classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first one is the so-called “kernel bandwidth (c).” The second one is a “penalty factor (υ)” that influences the trade-off between the margin and errors. In this study, Gaussian radial kernel function (Kakde et al , 2018) is recommended and expressed as:…”

Section: Proposed Occ-svm Single Multivariate Chart Approach For Simu...mentioning

confidence: 99%

“…If the value of the penalty factor (υ) is held constant, the number of support vectors (e.g. that define the boundary of an SVM) will depend on the bandwidth parameter c (Kakde et al , 2018). A popular strategy for selecting an appropriate value of c is by grid search and cross-validation technique (Mahadevan and Shah, 2009).…”

Section: Proposed Occ-svm Single Multivariate Chart Approach For Simu...mentioning

confidence: 99%

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An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics

Maji

Mukherjee

2021

IJQRM

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PurposeThe purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.Design/methodology/approachThe step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.FindingsA comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.Research limitations/implicationsThe sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.Practical implicationsThe proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.Originality/valueVarious multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

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Section: Proposed Occ-svm Single Multivariate Chart Approach For Simu...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Occ-svm Single Multivariate Chart Approach For Simu...mentioning

confidence: 99%

Section: Proposed Occ-svm Single Multivariate Chart Approach For Simu...mentioning

confidence: 99%

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An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics

Maji

Mukherjee

2021

IJQRM

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“…The utility of this one-class that is based on the establishment of a kernel, proves in its effectiveness to detect outliers and its high accuracy. To add to this, the SVDD has proved its robustness in various applications related to different domains [18,19,20,21,22,23,24], in the case where no prior knowledge on the distribution of the data is available [25]. This is due to its criterion of taking into consideration only those samples that belong to the target class in order to train the underlying data distribution.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis System of Alzheimer’s Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model

2019

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An improved computer-aided diagnosis (CAD) system is proposed for the early diagnosis of Alzheimer’s disease (AD) based on the fusion of anatomical (magnetic resonance imaging (MRI)) and functional (8F-fluorodeoxyglucose positron emission tomography (FDG-PET)) multimodal images, and which helps to address the strong ambiguity or the uncertainty produced in brain images. The merit of this fusion is that it provides anatomical information for the accurate detection of pathological areas characterized in functional imaging by physiological abnormalities. First, quantification of brain tissue volumes is proposed based on a fusion scheme in three successive steps: modeling, fusion and decision. (1) Modeling which consists of three sub-steps: the initialization of the centroids of the tissue clusters by applying the Bias corrected Fuzzy C-Means (FCM) clustering algorithm. Then, the optimization of the initial partition is performed by running genetic algorithms. Finally, the creation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissue maps by applying the Possibilistic FCM clustering algorithm. (2) Fusion using a possibilistic operator to merge the maps of the MRI and PET images highlighting redundancies and managing ambiguities. (3) Decision offering more representative anatomo-functional fusion images. Second, a support vector data description (SVDD) classifier is used that must reliably distinguish AD from normal aging and automatically detects outliers. The “divide and conquer” strategy is then used, which speeds up the SVDD process and reduces the load and cost of the calculating. The robustness of the tissue quantification process is proven against noise (20% level), partial volume effects and when inhomogeneities of spatial intensity are high. Thus, the superiority of the SVDD classifier over competing conventional systems is also demonstrated with the adoption of the 10-fold cross-validation approach for synthetic datasets (Alzheimer disease neuroimaging (ADNI) and Open Access Series of Imaging Studies (OASIS)) and real images. The percentage of classification in terms of accuracy, sensitivity, specificity and area under ROC curve was 93.65%, 90.08%, 92.75% and 97.3%; 91.46%, 92%, 91.78% and 96.7%; 85.09%, 86.41%, 84.92% and 94.6% in the case of the ADNI, OASIS and real images respectively.

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Efficient SVDD sampling with approximation guarantees for the decision boundary

et al. 2022

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Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.

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A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

Cited by 6 publications

References 19 publications

An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics

An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics

Computer-Aided Diagnosis System of Alzheimer’s Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model

Efficient SVDD sampling with approximation guarantees for the decision boundary

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