Linear dimensionality reduction based on Hybrid structure preserving projections

Zhang, Yupei; Xiang, Ming; Yang, Bo

doi:10.1016/j.neucom.2015.07.011

Cited by 26 publications

(18 citation statements)

References 47 publications

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“…KLDA algorithm maps the input vectors to a higher dimensional feature space F via the nonlinear mapping function ∅, and then it executes the linear discriminant analysis in the high dimensional feature space [ 40 ]. To increase further understanding, the KLDA algorithm will be described in detail in the S1 File , in which, these two literatures [ 41 , 42 ] will be cited.…”

Section: Methodsmentioning

confidence: 99%

Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm

Wang

Yue

2018

PLoS ONE

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A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate. Specifically, based on the position-specific scoring matrix (PSSM), we proposed a new expression, correlation position-specific scoring matrix (CoPSSM) as the protein feature representation. Based on the classic nonlinear dimension reduction algorithm, kernel linear discriminant analysis (KLDA), we added a new discriminant criterion and proposed a dichotomous greedy genetic algorithm (DGGA) to intelligently select its kernel bandwidth parameter. Two public datasets with Jackknife test and KNN classifier were used for the numerical experiments. The results showed that the overall success rate (OSR) with single representation CoPSSM is larger than that with many relevant representations. The OSR of the proposed method can reach as high as 87.444% and 90.3361% for these two datasets, respectively, outperforming many current methods. To show the generalization of the proposed algorithm, two extra standard datasets of protein subcellular were chosen to conduct the expending experiment, and the prediction accuracy by Jackknife test and Independent test is still considerable.

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Section: Methodsmentioning

confidence: 99%

Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm

Wang

Yue

2018

PLoS ONE

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“…For the focus of this paper, we do not give too many descriptions for the derivation and calculation process of matrix W* . According to [ 31 , 32 ], for multi-class pattern classification, such as C classification problem, the orthonormal columns of W * must satisfy Equation (13), which is a generalized eigenvalue problem.…”

Section: Dimension Reduction Methods and Classifier Algorithmmentioning

confidence: 99%

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

Wang

Liu

2015

IJMS

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An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

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“…(2) Regression problem. By taking the grade as the response variable, SGP is rewritten into assigning scores following the features of student or course, such as linear regression [5,11,12], neural network [13][14][15] and random forest [9]. (3) Matrix completion.…”

Section: Introductionmentioning

confidence: 99%

Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

Zhang

Dai

et al. 2020

Applied Sciences

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Student grade prediction (SGP) is an important educational problem for designing personalized strategies of teaching and learning. Many studies adopt the technique of matrix factorization (MF). However, their methods often focus on the grade records regardless of the side information, such as backgrounds and relationships. To this end, in this paper, we propose a new MF method, called graph regularized robust matrix factorization (GRMF), based on the recent robust MF version. GRMF integrates two side graphs built on the side data of students and courses into the objective of robust low-rank MF. As a result, the learned features of students and courses can grasp more priors from educational situations to achieve higher grade prediction results. The resulting objective problem can be effectively optimized by the Majorization Minimization (MM) algorithm. In addition, GRMF not only can yield the specific features for the education domain but can also deal with the case of missing, noisy, and corruptive data. To verify our method, we test GRMF on two public data sets for rating prediction and image recovery. Finally, we apply GRMF to educational data from our university, which is composed of 1325 students and 832 courses. The extensive experimental results manifestly show that GRMF is robust to various data problem and achieves more effective features in comparison with other methods. Moreover, GRMF also delivers higher prediction accuracy than other methods on our educational data set. This technique can facilitate personalized teaching and learning in higher education.

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Linear dimensionality reduction based on Hybrid structure preserving projections

Cited by 26 publications

References 47 publications

Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm

Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

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