Multiple Kernel Fuzzy Clustering With Unsupervised Random Forests Kernel and Matrix-Induced Regularization

Zhao, Yin-Ping; Chen, Long; Gan, Min; Chen, C. L. Philip

doi:10.1109/access.2018.2889185

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…Most known applications of fuzzy clustering include marketing segmentation, image analysis and bioinformatics (Grover, 2014; Li and Lewis, 2016). Fuzzy C-mean is the mostly used fuzzy clustering algorithm but it faces problems in handling high-dimensional and large data sets (Grover, 2014; Saxena et al , 2017; Zhao et al , 2018).…”

Section: Potential Of Cluster Analysis For Project Categorizationmentioning

confidence: 99%

A Data-driven project categorization process for portfolio selection

bok

Berrado

2021

JM2

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Purpose Categorizing projects allows for better alignment of a portfolio with the organizational strategy and goals. An appropriate project categorization helps understand portfolio’s structure and enables proper project portfolio selection (PPS). In practice, project categorization is, however, conducted in intuitive approaches. Furthermore, little attention has been given to project categorization methods in the project management literature. The purpose of this paper is to provide researchers and practitioners with a data-driven project categorization process designed for PPS. Design/methodology/approach The suggested process was modeled considering the main characteristics of project categorization systems revealed from the literature. The clustering analysis is used as the core-computing technology, allowing for an empirically based categorization. This study also presents a real-world case study in the automotive industry to illustrate the proposed approach. Findings This study confirmed the potential of clustering analysis for a consistent project categorization. The most important attributes that influenced the project grouping have been identified including strategic and intrinsic features. The proposed approach helps increase the visibility of the portfolio’s structure and the comparability of its components. Originality/value There is a lack of research regarding project categorization methods, particularly for the purpose of PPS. A novel data-driven process is proposed to help mitigate the issues raised by prior researchers including the inconsistencies, ambiguities and multiple interpretations related to the taken-for-granted categories. The suggested approach is also expected to facilitate projects evaluation and prioritization within appropriate categories and contribute in PPS effectiveness.

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Section: Potential Of Cluster Analysis For Project Categorizationmentioning

confidence: 99%

A Data-driven project categorization process for portfolio selection

bok

Berrado

2021

JM2

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“…In unsupervised learning, some methods extend single kernel based clustering method into multiple kernel setting. For example, Zhao et al [20] proposed a multiple kernel fuzzy clustering method by introducing a matrix-induced regularization; Kang et al [21] provided a self-weighted multiple kernel learning algorithm and applied it to a graph-based clustering method. Since kernel k-means is a popular clustering method, many unsupervised multiple kernel learning methods have been developed in the framework of it.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

Zhou

Fan

2019

IEEE Access

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Kernel-based clustering methods can capture the non-linear structure and identify arbitrarily shaped clusters, so they have been widely used in machine learning tasks. Since the performance of kernel methods critically depends on the choices of kernels, multiple kernel learning methods are proposed to alleviate the effort for kernel designing. The conventional multiple kernel learning methods learn a consensus kernel by linearly combining all candidate kernels, whereas ignoring the influence of the noises. To improve the robustness of multiple kernel learning methods, in this paper, we analyze the local and global noises and design regularized terms to characterize them respectively. Then, we propose a novel local and global de-noising multiple kernel learning method which can explicitly extract the local and global noises to recover the clean kernels for multiple kernel learning. After that, a block coordinate descent algorithm is presented to solve the optimization problem. Finally, the extensive experiments on benchmark data sets will demonstrate the effectiveness of the proposed algorithm. INDEX TERMS Multiple Kernel learning, robust learning, tensor decomposition.

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“…Recently, a number of multi-view clustering methods [16]- [23] have been proposed and have been proved to be effective in solving multi-view clustering problems. However, existing multi-view clustering methods typically solve a non-convex optimization problem [24], which results in the consequence that they get trapped in bad local minima easily.…”

Section: Introductionmentioning

confidence: 99%

Self-Paced Multi-View Clustering via a Novel Soft Weighted Regularizer

et al. 2019

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Multi-view clustering (MVC), which can exploit complementary information of different views to enhance the clustering performance, has attracted people's increasing attentions in recent years. However, existing multi-view clustering methods typically solve a non-convex problem, therefore are easily stuck into bad local minima. In addition, noisy data and outliers affect the clustering process negatively. In this paper, we propose self-paced multi-view clustering via a novel soft weighted regularizer (SPMVC) to address these issues. Specifically, SPMVC progressively selects samples to train the MVC model from simplicity to complexity in a self-paced manner. A novel soft weighted regularizer is proposed to further reduce the negative impact of outliers and noisy data. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method. INDEX TERMS Multi-view clustering, self-paced learning, soft weighting.

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Multiple Kernel Fuzzy Clustering With Unsupervised Random Forests Kernel and Matrix-Induced Regularization

Cited by 8 publications

References 41 publications

A Data-driven project categorization process for portfolio selection

A Data-driven project categorization process for portfolio selection

Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

Self-Paced Multi-View Clustering via a Novel Soft Weighted Regularizer

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