scholarly journals Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Chengzhang Zhu ◽  
Xinwang Liu ◽  
Qiang Liu ◽  
Yuewei Ming ◽  
Jianping Yin
Keyword(s):  
Extreme Learning Machine ◽  
High Efficiency ◽  
Computational Cost ◽  
Multiple Kernel Learning ◽  
Superior Performance ◽  
Kernel Learning ◽  
Normal Vector ◽  
Learning Approach ◽  
Multiple Kernel ◽  
Learning Machine

We propose a distance based multiple kernel extreme learning machine (DBMK-ELM), which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, anl2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM). Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.

A three-level Multiple-Kernel Learning approach for soil spectral analysis

2020 ◽  
Vol 389 ◽  
pp. 27-41 ◽  
Author(s):  
Nikolaos L. Tsakiridis ◽  
Christos G. Chadoulos ◽  
John B. Theocharis ◽  
Eyal Ben-Dor ◽  
George C. Zalidis
Keyword(s):  
Spectral Analysis ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Learning Approach ◽  
Multiple Kernel

A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data

2020 ◽  
Author(s):  
Ren Qi ◽  
Jin Wu ◽  
Fei Guo ◽  
Lei Xu ◽  
Quan Zou
Keyword(s):  
Single Cell ◽  
Spectral Clustering ◽  
Kernel Method ◽  
Multiple Kernel Learning ◽  
Superior Performance ◽  
Kernel Learning ◽  
Support Vector ◽  
Multiple Kernel ◽  
Proposed Model ◽  
Similarity Information

Abstract Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https://github.com/Cuteu/SMSC/.

scholarly journals Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1148 ◽  
Author(s):  
Rei Sonobe
Keyword(s):  
Extreme Learning Machine ◽  
Local Governments ◽  
Multiple Kernel Learning ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Multiple Kernel ◽  
Crop Classification ◽  
Learning Machine ◽  
Simple Ratio

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

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