Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

Zhou, Peng; Fan, Ye; Du, Liang

doi:10.1109/access.2019.2904727

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Note that the proposed algorithm integrates the MKL with local and global structure learning and the Hilbert space selfexpressiveness property in a unified optimization problem. A denoising MKL method was presented by Zhou et al [59]. It considers two kinds of noise: local noise, which appears in a small number of elements of the kernel matrix and is often induced by outliers or corrupted instances, and global noise, which appears in most of the elements of the kernel matrix and is often induced by inappropriate kernels.…”

Section: B Background Reviewmentioning

confidence: 99%

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

2021

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Wind power has contributed significantly to the increase in electricity generation, but a decision-making tool capable of dealing with its variability and limited predictability is necessary. For this purpose, a novel self-adaptive approach for kernel recursive least-squares machines named multiple challengers is introduced in this work, which is successfully used to produce very short-term wind power forecasts at eight wind farms in Australia. The proposed method is based on a competitive tracking method, and the algorithm deals with some common difficulties of kernel methods, e.g., the increasing kernel matrix size associated with time and memory complexities and the overfitting problem. The proposed method always considers the new information received by the model, thus identifying changes in the time series, avoiding abrupt loss of information and maintaining a controlled number of examples since there is an adaptive selection of the active kernel. It works with the smallest dictionary possible, reducing the probability of overfitting. Five minute-ahead wind power forecasts are produced and evaluated in terms of point forecast skill scores and calibration. The results of the proposed method are compared with those provided by other kernel-based versions of the recursive least-squares algorithm, an online version of the extreme learning machine method, and the persistence time series model. An increase in the number of kernels used in the ensemble system can lead to better results when compared with those of single-kernel models.INDEX TERMS Multiple kernel learning, online training, renewable energy, wind power forecasting.

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Section: B Background Reviewmentioning

confidence: 99%

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

2021

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“…On the one hand, these datasets provide opportunities to reveal new insights into many biological problems, e.g., elucidating cell types, on the other hand, there are also computational challenges due to the amount of data. A straightforward approach to elucidate cell types in complex tissues is to partition the cells into some separated subgroups via clus-tering techniques [10]- [13], which can be regarded as an unsupervised classification problem [14]- [16]. Many previous clustering techniques can be used for this task, such as principal component analysis (PCA) [17], spectral clustering [18], and k-means [19].…”

Section: Introductionmentioning

confidence: 99%

Single-Cell RNA-Sequencing Data Clustering via Locality Preserving Kernel Matrix Alignment

et al. 2020

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Single-cell RNA-sequencing (scRNA-seq) data provide opportunities to reveal new insights into many biological problems such as elucidating cell types. An effective approach to elucidate cell types in complex tissues is to partition the cells into several separated subgroups via clustering techniques, where the cells in a specific cluster belong to the same cell type based on gene expression patterns. In this work, we present a novel multiple kernel clustering framework for scRNA-seq data clustering via locality preserving kernel alignment. Specifically, we first generate a series of similarity kernel matrices by using different kernel functions. Then we transfer the clustering task to a multiple kernel k-means problem with the kernels aligned in a local manner, i.e., the similarity of a sample to its k-nearest neighbours are aligned with the ideal similarity matrix. In our method, the clustering process focuses on closer sample pairs that shall stay together, and avoids involving unreliable similarity evaluation for farther sample pairs. In addition, we construct a local Laplacian matrix for each sample to constrain that closer samples should be allocated similar labels. In such a manner, the local structure of the data can be well preserved and utilized to produce better alignment for clustering. An alternate updating algorithm with theoretical analysis is developed to solve the proposed problem. We evaluate the performance of the proposed method on various real scRNAseq data, and the results show that our method can obtain superior results when compared with other stateof-the-art approaches. INDEX TERMS scRNA-seq clustering, elucidating cell types, tissues, similarity kernel matrices, locality preservation.

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“…The typical methods include K-means based [2,4,7,18,20,32,37,41], self-organizing map (SOM) [24], maximum margin clustering based [26,30,34], local learning-based [33], spectral clustering based [1,3,6,12,13,22,29] and subspace clustering based [8-10, 39, 40] algorithms. Compared with the single kernel counterpart, MKC should take special effort to handle the additional data problems such as noisy and incomplete kernels [2,15,19,21,28,36,38,40,41].…”

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confidence: 99%

Manifold Adaptive Multiple Kernel K-Means for Clustering

Zhang

Ren

et al. 2020

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Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the nonlinear relationship within kernels, whereas the local manifold structure among multiple kernel space is not sufficiently considered. In this paper, we adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. Thus, the induced multiple manifold adaptive kernels not only reflect the nonlinear relationship but also the local manifold structure. We then perform multiple kernel clustering within the multiple kernel k-means clustering framework. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets. CCS CONCEPTS• Theory of computation → Kernel methods.

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Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

Cited by 8 publications

References 22 publications

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

Single-Cell RNA-Sequencing Data Clustering via Locality Preserving Kernel Matrix Alignment

Manifold Adaptive Multiple Kernel K-Means for Clustering

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