An integrated optimisation algorithm for feature extraction, dictionary learning and classification

Cui, Yan; Jiang, Jielin; Lai, Zhihui; Hu, Zuojin

doi:10.1016/j.neucom.2017.11.043

Cited by 6 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 0]. Let X i = X − X i , after some deductions, we can obtain the following closed-form solution to ProCRC, α = Ty (7) where…”

Section: Collaborative Representation Based Classificationmentioning

confidence: 99%

“…Motivated by the recent development of sparse representation, Qiao et al 6 presented a dimensionality reduction technique called sparsity preserving projections (SPP). To make SRC efficiently deal with high-dimensional data, Cui et al 7 proposed an integrated optimisation algorithm to implement feature extraction, dictionary learning and classification simultaneously. To tackle the corrupted data, Xie et al 8 explored a dimensionality reduction method termed lowrank sparse preserving projections (LSPP) by combining the manifold learning and low-rank sparse representation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A sparsity augmented probabilistic collaborative representation based classification method

Cai

Yin

2019

Preprint

View full text Add to dashboard Cite

In order to enhance the performance of image recognition, a sparsity augmented probabilistic collaborative representation based classification (SA-ProCRC) method is presented. The proposed method obtains the dense coefficient through ProCRC, then augments the dense coefficient with a sparse one, and the sparse coefficient is attained by the orthogonal matching pursuit (OMP) algorithm. In contrast to conventional methods which require explicit computation of the reconstruction residuals for each class, the proposed method employs the augmented coefficient and the label matrix of the training samples to classify the test sample. Experimental results indicate that the proposed method can achieve promising results for face and scene images. The source code of our proposed SA-ProCRC is accessible at https://github.com/yinhefeng/SAProCRC.

show abstract

“…, 0]. Let X i = X − X i , after some deductions, we can obtain the following closed-form solution to ProCRC, α = Ty (7) where…”

Section: Collaborative Representation Based Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A sparsity augmented probabilistic collaborative representation based classification method

Cai

Yin

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The main tasks of this paper are extracting the effective features of well-testing data and optimizing the structure of the integration model. When classifying the data obtained under complicated working conditions, the classification accuracy tends to be poor by using single feature extraction or above-described classification methods [40]. Therefore, in order to improve the stage classification performance (that is, the characterizing information ability of data features and the generalization ability of classification models), we propose the deep vector learning model (DVLM).…”

Section: Introductionmentioning

confidence: 99%

Automatic Deep Vector Learning Model Applied for Oil-Well-Testing Feature Mining, Purification and Classification

Feng

Ren

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Well-testing stage analysis is an important way for oilfield operation state decision-making and reservoir management. However, due to the variability and nonlinearity of the downhole data caused by the complex exploration activities and the differences of petroleum type and geological conditions, the classical methods are ineffective in feature extraction, learning network construction, and classifier optimization. In this work, we propose a new well-testing stage classification method based on a deep vector learning model (DVLM). The novelty of this study lies in the combination of multi-feature extraction, deep learning and feature vector mapping. The proposed method can overcome the problems of poor feature representation ability and poor classification model generalization ability in the existing machine learning methods, which mainly caused by the non-optimized training network structure and the unreasonable classifier design,. Firstly, the initial features are obtained by four classical methods. Then a five layers deep belief network embedded with the mutual information coefficient method is implemented for further feature extraction and purification. Finally, the optimized learning vector quantization classifier outputs the predicted tags. For model training and testing 572 field samples total of 4004 data streams are used. By considering the classification errors and accuracy metrics, the neurons number of deep learning network and the classifier are tuned, and an optimal and stable framework is obtained. Comparative experimental results with several classical integration models show that the proposed model achieves the highest classification accuracy of 98.065% as well as the least of features (nine). The results demonstrate that the proposed model has excellent performance in improving the classification accuracy and completing the feature compression. Moreover, the proposed model has very important practical significance for guiding the automatic analysis and processing oil and gas data. INDEX TERMS Well-testing stage analysis; Feature extraction; Deep belief network; Learning vector quantization.

show abstract

“…To make SRC efficiently deal with high-dimensional data, Cui et al. 7 proposed an integrated optimization algorithm to implement feature extraction, dictionary learning, and classification simultaneously. To tackle the corrupted data, Xie et al.…”

Section: Introductionmentioning

confidence: 99%

A sparsity augmented probabilistic collaborative representation based classification method

Cai

Yin

2020

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

show abstract

An integrated optimisation algorithm for feature extraction, dictionary learning and classification

Cited by 6 publications

References 29 publications

A sparsity augmented probabilistic collaborative representation based classification method

A sparsity augmented probabilistic collaborative representation based classification method

Automatic Deep Vector Learning Model Applied for Oil-Well-Testing Feature Mining, Purification and Classification

A sparsity augmented probabilistic collaborative representation based classification method

Contact Info

Product

Resources

About