Sparse multinomial kernel discriminant analysis (sMKDA)

Harrison, Robert F.; Pasupa, Kitsuchart

doi:10.1016/j.patcog.2009.01.025

Cited by 9 publications

(5 citation statements)

References 36 publications

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“…Methods in the nature-inspired group are such as binary particle swarm optimization, GA [8,9], binary flower pollination [10], and binary cuckoo search [11]. The wrapper method of our interest was GA. Lastly, there have been extensive researches on embedded methods such as sparsity control by using l q -norm [12], Jeffrey's Hyperprior [13], canonical variate analysis [14]. Moreover, the common embedded methods for bioinformatic tasks are such as random forest, weight vector of SVM, and decision tree [4,7], but they were not used in our work because filter and wrapper methods have been reported to be more stable for feature selection task [7].…”

Section: Introductionmentioning

confidence: 99%

Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique

2020

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Background: The number of porcine Single Nucleotide Polymorphisms (SNPs) used in genetic association studies is very large, suitable for statistical testing. However, in breed classification problem, one needs to have a much smaller porcine-classifying SNPs (PCSNPs) set that could accurately classify pigs into different breeds. This study attempted to find such PCSNPs by using several combinations of feature selection and classification methods. We experimented with different combinations of feature selection methods including information gain, conventional as well as modified genetic algorithms, and our developed frequency feature selection method in combination with a common classification method, Support Vector Machine, to evaluate the method's performance. Experiments were conducted on a comprehensive data set containing SNPs from native pigs from America, Europe, Africa, and Asia including Chinese breeds, Vietnamese breeds, and hybrid breeds from Thailand. Results: The best combination of feature selection methods-information gain, modified genetic algorithm, and frequency feature selection hybrid-was able to reduce the number of possible PCSNPs to only 1.62% (164 PCSNPs) of the total number of SNPs (10,210 SNPs) while maintaining a high classification accuracy (95.12%). Moreover, the near-identical performance of this PCSNPs set to those of bigger data sets as well as even the entire data set. Moreover, most PCSNPs were well-matched to a set of 94 genes in the PANTHER pathway, conforming to a suggestion by the Porcine Genomic Sequencing Initiative. Conclusions: The best hybrid method truly provided a sufficiently small number of porcine SNPs that accurately classified swine breeds.

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Section: Introductionmentioning

confidence: 99%

Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique

2020

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“…where M n = P T n P n + Λ n The computation involved in (22) is unacceptably large, to further simplify the LOO error calculation, a residual matrix is introduced as:…”

Section: B Locally Regularized Forward Selectionmentioning

confidence: 99%

“…and h i is the i th diagonal element of the inverse recursive matrix M −1 in (22). Suppose h k = diag(M −1 k ) is defined, then this vector can be updated recursively as follows (see Appendix for more details):…”

Section: Updating Regularisation Parametersmentioning

confidence: 99%

“…Though the automatic forward selection and learning algorithm is introduced for binomial classification problems in this paper, it can be easily extended to multinomial problems [22]. Further, this new algorithm can also be easily generalized to a wide range of nonlinear models that have a linear-in-theparameters structure, such as Nonlinear Autoregressive with Exogenous input (NARX) and basis radial function (RBF) neural networks.…”

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confidence: 99%

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A fast automatic construction algorithm for kernel fisher discriminant classifiers

Deng

Irwin

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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Nonlinear Fisher Discriminant Analysis for binomial problems can be converted into a Linear-In-TheParameters classifier model by introducing a least-squares cost function. However, the complexity of the classifier scales with the number of training samples, which makes it difficult to use on large data sets. A popular solution is to adopt a sub-model selection approach, such as Orthogonal Least Squares (OLS) or the Fast Recursive Algorithm (FRA), to produce a compact classifier with accurate parameters. The problem is that these methods need additional subjective choice of selection termination criterion, and inappropriate choice of this criterion may lead to an over-fitting classifier. Further, training data with large noise may even deteriorate the performance. This paper proposes a fast automatic forward algorithm for constructing a parsimonious descriptor of the nonlinear discriminant function, thus both the subjective choice of the termination criterion and the over-fitting problem due to noisy data can be avoided. This is achieved by an effective integration of the Bayesian regularisation technique, the Leave-One-Out (LOO) cross-validation criterion and the FRA algorithm. Experimental results are included to confirm the efficacy and superiority of the proposed algorithm on both artificial and real world data sets.

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“…All such relative comparisons derived in each neighborhood on the manifold are enumerated and maintained in lowdimensional manifold to be learned. Sparse Multinomial Kernel Discriminant Analysis (sMKDA) [13] is a method for sparse, multinomial kernel discriminant analysis. It is based on the connection between Canonical Variate Analysis (CVA) [14] and least-squares and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems.…”

Section: Introductionmentioning

confidence: 99%

A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification

Zhao

2014

Journal of Applied Mathematics

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We propose a novel method, called Kernel Neighborhood Discriminant Analysis (KNDA), which can be regarded as a supervised kernel extension of Locality Preserving Projection (LPP). KNDA nonlinearly maps the original data into a kernel space in which two graphs are constructed to depict the within-class submanifold and the between-class submanifold. Then a criterion function which minimizes the quotient between the within-class representation and the between-class representation of the submanifolds is designed to separate each submanifold constructed by each class. The real contribution of this paper is that we bring and extend the submanifold based algorithm to a general model and by some derivation a simple result is given by which we can classify a given object to a predefined class effectively. Experiments on the MNIST Handwritten Digits database, the Binary Alphadigits database, the ORL face database, the Extended Yale Face Database B, and a downloaded documents dataset demonstrate the effectiveness and robustness of the proposed method.

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Sparse multinomial kernel discriminant analysis (sMKDA)

Cited by 9 publications

References 36 publications

Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique

Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique

A fast automatic construction algorithm for kernel fisher discriminant classifiers

A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification

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