An automated PCA-based approach towards optimization of the rational function model

Gholinejad, Saeid; Amiri-Simkooei, A. R.; Moghaddam, Sayyed Hamed Alizadeh; Naeini, Amin Alizadeh

doi:10.1016/j.isprsjprs.2020.05.011

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…The visibility term and the disparity discontinuity term were iteratively used to continuously detect occlusions and optimize the boundary disparity. Finally, the fractal net evolution was used to optimize the disparity results to output the final disparity map, and the corresponding DSM was generated using the rational function model (RFM) [46].…”

Section: Methodsmentioning

confidence: 99%

Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

Yang

Chen

2023

Remote Sensing

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Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the correspondence between matching points. To address the above problems, a novel generalized stereo matching method based on the iterative optimization of hierarchical graph structure consistency cost is proposed for urban 3D scene reconstruction. First, the self-similarity of images is used to construct k-nearest neighbor graphs. The left-view and right-view graph structures are mapped to the same neighborhood, and the graph structure consistency (GSC) cost is proposed to evaluate the similarity of the graph structures. Then, cross-scale cost aggregation is used to adaptively weight and combine multi-scale GSC costs. Next, object-based iterative optimization is proposed to optimize outliers in pixel-wise matching and mismatches in disparity discontinuity regions. The visibility term and the disparity discontinuity term are iterated to continuously detect occlusions and optimize the boundary disparity. Finally, fractal net evolution is used to optimize the disparity map. This paper verifies the effectiveness of the proposed method on a public US3D dataset and a self-made dataset, and compares it with state-of-the-art stereo matching methods.

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Section: Methodsmentioning

confidence: 99%

Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

Yang

Chen

2023

Remote Sensing

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“…In this study, principal component analysis (PCA) was implemented because it has been reported to aid the achievement of high model performance [70]- [72]. PCA operates by reducing dimensionality through the transformation of data into a new coordinate system which appears as a linear combination of the original features.…”

Section: Feature Extractionmentioning

confidence: 99%

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Jumanto,

Rofik,

Sugiharti

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics. Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

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Section: Related Workmentioning

confidence: 99%

“…It is known that the simultaneous determination of the RPCs is an ill-posed problem [15][18] [20] . Some direct methods resolve the problem through the use of parameter selection strategies, such as estimation based on scatter matrix and stepwise regression [18] and optimization based on principal component analysis [20] , to update only a subset of the original RPCs. Whereas the regularization-based methods try to optimize a constrained version of RPCs, such as the L1-norm minimization [19] , the incremental discrete Kalman filtering [22] , the batch iterative least squares, and the sequential least squares [24] .…”

Section: Related Workmentioning

confidence: 99%

“…Despite the advantages of RFMs, it is hard to directly optimize the RPCs within a least-square framework due to strong correlations between the parameters. [8]- [11][18]- [20] It is of great theoretical significance and practical value to develop geometric sensor models with a set of adjustable parameters that can be fully recovered from RPCs and then be used in Nonlinear Systematic Distortions Compensation in Satellite Images Based on an Equivalent Geometric Sensor Model Recovered from RPCs Hui Cao, Pengjie Tao*, Yuxuan Liu, and Haihong Li S block adjustment to represent and analyze local image distortions caused by various systematic errors. The general form of the equivalent geometric sensor model (EGSM) was introduced and the EGSM-based approach of block adjustment with DEM as controls was presented for accurate geo-referencing stereo satellite images [21] .…”

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confidence: 99%

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Nonlinear Systematic Distortions Compensation in Satellite Images Based on an Equivalent Geometric Sensor Model Recovered From RPCs

Hui¹,

Tao²,

Liu³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An automated PCA-based approach towards optimization of the rational function model

Cited by 9 publications

References 18 publications

Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Nonlinear Systematic Distortions Compensation in Satellite Images Based on an Equivalent Geometric Sensor Model Recovered From RPCs

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