Kernel Matrix-Based Heuristic Multiple Kernel Learning

Anderson, Derek T.; Havens, Timothy C.; Price, Stanton R.

doi:10.3390/math10122026

Cited by 4 publications

(3 citation statements)

References 29 publications

(26 reference statements)

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“…Distance metric learning is a key aspect of metric learning, aiming to learn a function that can measure the distance between samples. Various methods exist for distance metric learning, including prototype-based methods (Gu et al, 2022b ), metric matrix-based methods (Price et al, 2022 ), and maximum margin-based methods (Li X. et al, 2022 ). Among these, Max-Margin Metric Learning (MMML) has emerged as a classic technique maximizing the distances between different classes while minimizing the distances within the same class.…”

Section: Related Workmentioning

confidence: 99%

Metric networks for enhanced perception of non-local semantic information

Zhou

Zhang

2023

Front. Neurorobot.

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IntroductionMetric learning, as a fundamental research direction in the field of computer vision, has played a crucial role in image matching. Traditional metric learning methods aim at constructing two-branch siamese neural networks to address the challenge of image matching, but they often overlook to cross-source and cross-view scenarios.MethodsIn this article, a multi-branch metric learning model is proposed to address these limitations. The main contributions of this work are as follows: Firstly, we design a multi-branch siamese network model that enhances measurement reliability through information compensation among data points. Secondly, we construct a non-local information perception and fusion model, which accurately distinguishes positive and negative samples by fusing information at different scales. Thirdly, we enhance the model by integrating semantic information and establish an information consistency mapping between multiple branches, thereby improving the robustness in cross-source and cross-view scenarios.ResultsExperimental tests which demonstrate the effectiveness of the proposed method are carried out under various conditions, including homologous, heterogeneous, multi-view, and crossview scenarios. Compared to the state-of-the-art comparison algorithms, our proposed algorithm achieves an improvement of ~1, 2, 1, and 1% in terms of similarity measurement Recall@10, respectively, under these four conditions.DiscussionIn addition, our work provides an idea for improving the crossscene application ability of UAV positioning and navigation algorithm.

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Section: Related Workmentioning

confidence: 99%

Metric networks for enhanced perception of non-local semantic information

Zhou

Zhang

2023

Front. Neurorobot.

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“…Jiang et al [23] proposed a high-order norm-product regularized multiple kernel learning framework to optimize the discrimination performance. Stanton R et al [24] explored different divergence measures on the values in the kernel matrices and reproducing kernel Hilbert space (RKHS). Fatemeh and Sattar [25] formulated multiple kernel learning in a bi-level learning paradigm consisting of the kernel combination weight learning (KWL) stage and the self-paced learning (SPL) stage.…”

Section: Related Workmentioning

confidence: 99%

Multi-Angle Fast Neural Tangent Kernel Classifier

Zhai

Liu

2022

Applied Sciences

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Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement.

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“…Even though multi-task learning improves accuracy performance, a similarity measure between tasks is highly www.ijacsa.thesai.org required, which is not a priority for many diseases [18]- [19]. The utilization of transcriptomics subjects possesses diverse challenges in interpretation.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

Swetha¹,

Srinivasan²,

P³

2023

IJACSA

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In recent years, Machine Learning (ML) techniques have been used by several researchers to classify diseases using gene expression data. Disease categorization using heterogeneous gene expression data is often used for defining critical problems such as cancer analysis. A variety of evaluated factors known as genes are used to characterize the gene expression data gathered from DNA microarrays. Accurate classification of genetic data is essential to provide accurate treatments to sick people. A large number of genes can be viewed simultaneously from the collected data. However, processing this data has some limitations due to noises, redundant data, frequent errors, increased complexity, smaller samples with high dimensionality, difficult interpretation, etc. A model must be able to distinguish the features in such heterogeneous data with high accuracy to make accurate predictions. So this paper presents an innovative model to overcome these issues. The proposed model includes an effective multiple indefinite kernel learning based model for analyze the gene expression microarray data, then an optimized kernel principal component analysis (OKPCA) to select best features and hybrid flow-directed arithmetic support vector machine (SVM)-based multiple infinite kernel learning (FDASVM-MIKL) model for classification. Flow direction and arithmetic optimization algorithms are combined with SVM to increase classification accuracy. The proposed technique has an accuracy of 99.95%, 99.63%, 99.60%, 99.51%, and 99.79% using the datasets including colon, Isolet, ALLAML, Lung_cancer, and Snp2 graph.

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Kernel Matrix-Based Heuristic Multiple Kernel Learning

Cited by 4 publications

References 29 publications

Metric networks for enhanced perception of non-local semantic information

Metric networks for enhanced perception of non-local semantic information

Multi-Angle Fast Neural Tangent Kernel Classifier

A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

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