Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized dat...
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Transfer learning is an important technology in addressing the problem that labeled data in a target domain are difficult to collect using extensive labeled data from the source domain. Recently,an algorithm named gra...
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Transfer learning is an important technology in addressing the problem that labeled data in a target domain are difficult to collect using extensive labeled data from the source domain. Recently,an algorithm named graph co-regularized transfer learning(GTL) has shown a competitive performance in transfer learning. However, its convergence is affected by the used approximate scheme, degenerating learned results. In this paper, after analyzing convergence conditions, we propose a novel update rule using the multiplicative update rule and develop a new algorithm named improved GTL(IGTL) with a strict convergence guarantee. Moreover, to prove the convergence of our method, we design a special auxiliary function whose value is intimately related to that of the objective function. Finally, the experimental results on the synthetic dataset and two real-world datasets confirm that the proposed IGTL is convergent and performs better than the compared methods.
Label noise is often contained in the training data due to various human factors or measurement errors,which significantly causes a negative effect on *** many previous methods that have been proposed to learn robust ...
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Label noise is often contained in the training data due to various human factors or measurement errors,which significantly causes a negative effect on *** many previous methods that have been proposed to learn robust classifiers,they are mainly based on the single-view *** the other hand,although existing multi-view classification methods benefit from the more comprehensive information,they rarely consider label *** this paper,we propose a novel label-noise robust classification model with multi-view learning to overcome these *** the proposed model,not only the classifier learning but also the label-noise removal can benefit from the multi-view ***,we relax the label matrix of the basic multi-view least squares regression model,and develop a nonlinear transformation with a natural probabilistic approximation in the process of labels,which is conveniently optimized and beneficial to improve the discriminative ability of ***,we preserve the intrinsic manifold structure of multi-view data on the relaxed label matrix,facilitating the process of label *** optimizing the proposed model with the nonlinear transformation,we derive a lemma about the partial derivation of the softmax related function,and develop an efficient alternating *** evaluations on six real-world datasets confirm the advantages of the proposed method,compared to the related state-of-the-art methods.
Non-negative matrix factorization(NMF) is a widely used technique for dimensionality reduction,and generalized separable NMF(GSNMF) can learn the representation with better interpretability,as it decomposes the given ...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Non-negative matrix factorization(NMF) is a widely used technique for dimensionality reduction,and generalized separable NMF(GSNMF) can learn the representation with better interpretability,as it decomposes the given matrix based on the row features and the column features at the same *** in some cases,the GSNMF algorithm faces the 0-K problem,where only one perspective of feature can be *** paper modified the generalized successive projection scheme,and proposes an improved generalized successive projection algorithm(IGSPA) to avoid the 0-K *** verify the effectiveness of our method,we conduct extensive experiments on three commonly used face *** with the existing methods,numerical experiments show that our method has superior performance.
Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically ...
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ISBN:
(数字)9781728190129
ISBN:
(纸本)9781728190136
Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically based on matrix factorization models. Recently, it is extended to the deep structure to exploit the hierarchical information of multi-view data, but the view-specific features and the label information are seldom considered. To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF). By integrating the partially shared deep decomposition structure, graph regularization and the semi-supervised regression model, PSDMF can learn a compact and discriminative representation through eliminating the effects of uncorrelated information. In addition, we develop an efficient iterative updating algorithm for PSDMF. Extensive experiments on five benchmark datasets demonstrate that PSDMF can achieve better performance than the state-of-the-art multi-view learning approaches. The MATLAB source code is available at https://***/libertyhhn/PartiallySharedDMF.
Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically ...
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