Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier

Phillips, Rhonda D.; Watson, Layne T.; Wynne, Randolph H.; Blinn, Christine E.

doi:10.1016/j.isprsjprs.2008.03.004

Cited by 32 publications

(19 citation statements)

References 25 publications

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“…The main purpose of attribute reduction is based on neighborhood rough set to eliminate redundant attributes classified data and extract useful information. Firms' business methods are changing from product-centric to (1) Singular value decomposition: This method has a good effect when the data dimension is high, and it is often used as a preprocessing method to realize the convergence of fuzzy rough reduction in high-dimensional data sets; but the calculation cost of this method is high [13,14]; (2) The principal component analysis is reduced, and the mutual influence between evaluation indexes is eliminated by replacing the original variables with several principal components with larger contributions. This study is necessary to delete irrelevant or unimportant attributes to eliminate the interference of irrelevant features when using the data with higher dimensions [15]; (3) Effective feature extraction in deep learning: Data-driven deep learning analysis has been developed and applied in many fields.…”

Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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“…Its applicability, though, in digital image processing, relies on the fact that multi-spectral remotely sensed spatial data are naturally redundant [33]. Applications include singal estimation in spectral data [34]; classification; data compression and noise reduction [35,36]; identification of spectral signatures [9]; and feature reduction [33].…”

Section: Image Processing Via Svdmentioning

confidence: 99%

“…To exemplify, [33] compared the performance of SVD to PCA as a feature reduction step on images containing fewer than 100 bands prior to forest/non-forest classifications. In their study, SVD outperformed PCA in terms of classification accuracy and computational time saving.…”

Section: Image Processing Via Svdmentioning

confidence: 99%

Remote sensing of burned areas via PCA, Part 1; centering, scaling and EVD vs SVD

Alexandris¹,

Gupta

Koutsias

2017

Open geospatial data, softw. stand.

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Background: Principal components analysis (PCA) is based conventially on the eigenvector decomposition (EVD). Mean-centering the input data prior to the eigenanalysis is treated as an integral part of the algorithm. It ensures that the first principal component is proportional to the maximum variance of the input data. Equivalent to EVD, but numerically more robust, is the singular value decomposition (SVD). Mean-centered data subjected to SVD, yield transformation coefficients identical to EVD. Nevertheless, mean-centering is optional in SVD. Avoiding to center the input data, results in generic first component that mainly reflects their mean. This may, however, detect more accurately distinct clusters in PCA-based change detection applications. Methods: In remote sensing, PCA transforms multi-spectral bands into a new coordinate system. The first, among the transformed components, contain the variance of unchanged landscape features. Succeeding components may contain an enhanced variance of changed features. Such is the case of burned surfaces appearing as distinct clusters in multitemporal composites. Conclusions: Within this framework, a non-centered SVD may increase the spectral separability of burned clusters among other features in some of the higher order principal components.

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“… SVD is of interest in this research due to its known performance in tolerating data noise (Simek, 2003;Simek et al, 2004;Phillips, Watson, Wynne, & Blinn, 2009;Chakroborty & Saha, 2010). It is a factorization of a real matrix ∈ × , ≥ ,…”

Section: Experiments Iii-feature Reduction Techniques Comparisonmentioning

confidence: 99%

A recommendation system for meta-modeling: A meta-learning based approach

Cui

Weir

et al. 2016

Expert Systems with Applications

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Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models are varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: 1) problem module, 2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, 3) meta-learner module which compares the performance of instance-based and model-based learning approaches for optimal framework design, and 4) performance evaluation module which introduces two criteria, Spearman's ranking correlation coefficient and hit ratio, to evaluate the system on the accuracy of model ranking prediction and the precision of the best model recommendation, respectively. To further improve the performance of meta-learning for meta-modeling recommendation, different types of feature reduction techniques, including singular value decomposition, stepwise regression and ReliefF, are studied. Experiments show that our proposed framework is able to achieve 94% correlation on model rankings, and a 91% hit ratio on best model recommendation. Moreover, the computational cost of metamodeling recommendation is significantly reduced from an order of minutes to seconds compared to traditional trial-and-error and ensemble process. The proposed framework can significantly advance the research in meta-modeling recommendation, and can be applied for data-driven system modeling.

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Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier

Cited by 32 publications

References 25 publications

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Remote sensing of burned areas via PCA, Part 1; centering, scaling and EVD vs SVD

A recommendation system for meta-modeling: A meta-learning based approach

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