Class-Specific Sparse Principal Component Analysis for Visual Classification

Pan, Fang; Zhang, Zai-Xu; Liu, Baodi; xie, Jijun

doi:10.1109/access.2020.3001202

Cited by 3 publications

(2 citation statements)

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“…A work by [14] uses class-specific feature extraction and principal component analysis for classification. They extend the traditional dictionary learning for all classes to specific classes.…”

Section: Related Workmentioning

confidence: 99%

A Class Specific Feature Selection Method for Improving the Performance of Text Classification

S B,

et al. 2024

SCPE

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Recently, a significant amount of research work has been carried out in the field of feature selection. Although these methods help to increase the accuracy of the machine learning classification, the selected subset of features considers all the classes and may not select recommendable features for a particular class. The main goal of our paper is to propose a new class-specific feature selection algorithm that is capable of selecting an appropriate subset of features for each class. In this regard, we first perform class binarization and then select the best features for each class. During the feature selection process, we deal with class imbalance problems and redundancy elimination. The Weighted Average Voting Ensemble method is used for the final classification. Finally, we carry out experiments to compare our proposed feature selection approach with the existing popular feature selection methods. The results prove that our feature selection method outperforms the existing methods with an accuracy of more than 37%.

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Section: Related Workmentioning

confidence: 99%

A Class Specific Feature Selection Method for Improving the Performance of Text Classification

S B,

et al. 2024

SCPE

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“…In the first category, it consists of two types of atoms, i.e., the class-specific atoms contributing to the representation of data samples belonging to a particular class and the atoms contributing to the representation of all samples of the data [8,26,27]. The second category consists of atoms grouped among classes, where each class of atoms represents the data belonging to that class only [3,14,[28][29][30]. In the third category of dictionaries, a data sample is represented as a sparse linear combination of atoms selected dictionary-wide without grouping the atoms among classes.…”

Section: Introductionmentioning

confidence: 99%

Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification

Riaz-ud-din,

Ghafoor,

Shafait

2023

Applied Sciences

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Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient.

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D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

et al. 2021

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Class-Specific Sparse Principal Component Analysis for Visual Classification

Cited by 3 publications

References 40 publications

A Class Specific Feature Selection Method for Improving the Performance of Text Classification

A Class Specific Feature Selection Method for Improving the Performance of Text Classification

Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification

D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

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