Fuzzy based affinity learning for spectral clustering

Li, Qilin; Ren, Yan; Li, Ling; Liu, Wanquan

doi:10.1016/j.patcog.2016.06.011

Cited by 30 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In AFS theory, the definition of membership function and the logic operation of fuzzy set are automatically generated according to the distributions of raw data and the semantics of the fuzzy concepts [25]. As a suitable approach to semantic concept extraction and interpretations for multiple attributes, the AFS theory has been extended to many issues of pattern recognition and machine learning, including fuzzy decision trees [26], [27], fuzzy rough sets [28], [29], fuzzy classifiers [23], [30] and fuzzy clustering [31], [32]. Meanwhile, this motivated that AFS theory has been applied to many practical problems, such as semantic facial descriptor extraction [4], management strategic analysis [33], predicting readmissions in intensive care unit [34], time series analysis [35], mitigating the risk of customer churn [36], handwritten number recognition [37], etc.…”

Section: Afs Theorymentioning

confidence: 99%

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Ren

Zhang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

The main purpose of this paper is to label one face to which it refers using a commonly accepted concept or linguistic term that corresponds to a given face class. Therefore, a facial semantic description method based on the axiomatic fuzzy set (AFS) theory is proposed. Firstly, the facial geometric features are defined based on the landmarks, such as distance features, angle features and scale features, etc. And then the salient features are selected via AFS feature selection method, and the fuzzy linguistic terms are defined the selected features. Thirdly, the facial descriptions which can characterize the salient characteristics are extracted via a clustering scheme in the framework of AFS theory. Multiple experiments are done on Chinese ethnic face database including Korean, Uyghur and Zhuang. The experimental results demonstrate that AFS feature selection can obtain higher accuracies via AFS clustering algorithm for each ethnic group than that via mRMR. Furthermore, compared with k-means, FCM and Minmax k-means, AFS clustering algorithm obtains the highest accuracies for each ethnic group, which verifies the efficacy of the proposed semantic extractor. Consequently, one comparative experimental results verify the consistency between the extracted semantic description method and the facial physical characteristics in physical anthropology. Also the facial semantics would be very useful to further describe some facial action units or facial expressions, which can be further used for affection analysis of images as well as semi-automatic action unit/facial expression annotation.

show abstract

Section: Afs Theorymentioning

confidence: 99%

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Ren

Zhang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the past decade, AFS theory has been developed forming the following important methodologies: AFS clustering (Liu, Wang, et al, ; Liu & Ren, ; Liu et al, ; Q. Li et al, ; Ren et al, ), AFS classification (Liu et al, ; Liu & Liu, ; Liu & Pedrycz, ; Ren et al, ), AFS rough set (Ebonzo & Liu, ; Liu et al, ; L. Wang, Ren, et al, ; L. Wang, et al, ), AFS formal concept analysis (L. Wang & Liu, ; L. Wang et al, ; L. Wang & Liu, ), and AFS pseudometric topology (Ding et al, ). This section discusses the above important researches of AFS methodology on knowledge discovery and semantic representation, from which we can see that AFS theory has a great potential to formalize human concepts and their semantic logic operations.…”

Section: Important Research Of Afs Methodologymentioning

confidence: 99%

“…The AFS clustering algorithm is summarized in Algorithm 1 (Liu, Wang, et al, ), which is the basic AFS clustering procedure. Various developments and variants of Algorithm 1 have been reported in the studies of Ding et al (), Ren et al (), Liu and Ren (), Liu et al (), Q. Li et al (), L. Wang, Ren, and Liu (), L. Wang, et al (), and Liu and Pedrycz (). Example is used to illustrate the procedure of AFS clustering algorithm and related developments.…”

Section: Important Research Of Afs Methodologymentioning

confidence: 99%

Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory

Liu

Jia

Wang

et al. 2018

WIREs Data Min & Knowl

Self Cite

View full text Add to dashboard Cite

Axiomatic fuzzy set (AFS) theory facilitates a way on how to transform data into fuzzy sets (membership functions) and implement their fuzzy logic operations, which provides a flexible and powerful tool for representing human knowledge and emulate human recognition process. In recent years, AFS theory has received increasing interest. In this survey, we report the current developments of theoretical research and practical advances in the AFS theory. We first review some notion and foundations of the theory with an illustrative example, then, we focus on the various extensions of AFS theory for knowledge discovery, including clustering, classification, rough sets, formal concept analysis, and other learning tasks. Due to its unique characteristics of semantic representation, AFS theory has been applied in multiple domains, such as business intelligence, computer vision, financial analysis, and clinical data analysis. This survey provides a comprehensive view of these advances in AFS theory and its potential perspectives. This article is categorized under: Technologies > Computational Intelligence

show abstract

“…tree [18], [19], fuzzy clustering [20], [21], and fuzzy classifiers [22], [23]. AFS-based technology has been applied in practical applications, such as semantic facial descriptor extraction [24], management strategic analysis [25], time series forecasting [26], prevent customer loss [27], etc.…”

Section: Introductionmentioning

confidence: 99%

The Semantic Classification Approach Base on Neural Networks

Shi

Zhao

2020

IEEE Access

View full text Add to dashboard Cite

This paper addresses the classification problem, and a semantic classification approach using neural networks is proposed. The approach embeds the theoretical findings of the axiomatic fuzzy set theory in neural networks. Complex concepts are extracted by neural networks, which means that the class description is formed analytically rather than by tuning parameters of constraint conditions. The experiments are carried out on five benchmark datasets and compared results with five other neural network-based classifiers. The experimental results show that the proposed approach produces high classification accuracy and has a better explanation.

show abstract

Fuzzy based affinity learning for spectral clustering

Cited by 30 publications

References 32 publications

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory

The Semantic Classification Approach Base on Neural Networks

Contact Info

Product

Resources

About