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Article
From Gaussian kernel density estimation to kernel methods
This paper explores how a kind of probabilistic systems, namely, Gaussian kernel density estimation (GKDE), can be used to interpret several classical kernel methods, including the well-known support vector ma...
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Article
Double indices-induced FCM clustering and its integration with fuzzy subspace clustering
As one of the most popular algorithms for cluster analysis, fuzzy c-means (FCM) and its variants have been widely studied. In this paper, a novel generalized version called double indices-induced FCM (DI-FCM) ...
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Article
Nonnegative matrix factorization with manifold regularization and maximum discriminant information
Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product ...
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Article
A local and global classification machine with collaborative mechanism
As an advanced local and global learning machine, the existing maxi–min margin machine (M4) still has its heavy time-consuming weakness. Inspired from the fact that covariance matrix of a dataset can characterize...
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Article
Incremental enhanced α-expansion move for large data: a probability regularization perspective
To deal with large data clustering tasks, an incremental version of exemplar-based clustering algorithm is proposed in this paper. The novel clustering algorithm, called Incremental Enhanced α-Expansion Move (IEE...
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Article
Generalized competitive agglomeration clustering algorithm
In this paper, a generalized competitive agglomeration (CA) clustering algorithm called entropy index constraints competitive agglomeration (EICCA) is proposed to avoid the drawback that the fuzziness index m in ...
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Article
v-soft margin multi-task learning logistic regression
Coordinate descent (CD) is an effective method for large scale classification problems with simple operations and fast convergence speed. In this paper, inspired by v-soft margin support vector machine and multi-...
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Article
Extreme vector machine for fast training on large data
Quite often, different types of loss functions are adopted in SVM or its variants to meet practical requirements. How to scale up the corresponding SVMs for large datasets are becoming more and more important ...
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Article
Multi-view local linear KNN classification: theoretical and experimental studies on image classification
When handling special multi-view scenarios where data from each view keep the same features, we may perhaps encounter two serious challenges: (1) samples from different views of the same class are less similar...
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Article
A fuzzy system with common linear-term consequents equivalent to FLNN and GMM
In this study, a novel Takagi–Sugeno–Kang (TSK) fuzzy system termed as CLT–TSK in which the consequent of each fuzzy rule owns a common linear term is exploited to demonstrate its four distinctive merits. They ar...
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Article
Self-paced and Bayes-decision-rule linear KNN prediction
While a testing sample may be first encoded linearly with labeled samples and then classified with KNN on the sum of the obtained weights of the samples in each class so as to avoid the consistent distribution...
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Article
A novel data-free continual learning method with contrastive reversion
While continual learning has shown its impressive performance in addressing catastrophic forgetting of traditional neural networks and enabling them to learn multiple tasks continuously, it still requires a la...
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Article
Semi-supervised fuzzy broad learning system based on mean-teacher model
Fuzzy broad learning system (FBLS) is a newly proposed fuzzy system, which introduces Takagi–Sugeno fuzzy model into broad learning system. It has shown that FBLS has better nonlinear fitting ability and faste...
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Article
Deep adversarial reconstruction classification network for unsupervised domain adaptation
Although the existing adversarial domain adaptation methods have been successfully applied in the unsupervised domain adaptation community, their performances may perhaps be weakened due to a significant distr...