NOISE-ROBUST SOFT CLUSTERING OF GENE EXPRESSION TIME-COURSE DATA Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gen... MATTHIAS E. FUTSCHIK,BRONWYN CARLISLE - 《J Bioinform Comput Biol》 被引量...
Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D In order to achieve robust performance of preserving significant image details while removing noise for image segmentation, this paper presents a multi-obj... M Zhang,L Jiao,W Ma,... - 《Applied Soft Computing》 被...
The proposed VAD method is based on a soft-decision clustering approach built over a ratio of subband energies that improves recognition performance in noisy environments. The accuracy of the FCM-VAD algorithm lies in the use of a decision function defined over a multiple-observation (MO) window...
facilitating the process of label relaxation.For 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 algorithm.Experim...
Soft-decision-driven sparse channel estimation and turbo equalization for MIMO underwater acoustic communications IEEE Access (2018) J. Li et al. Efficient use of space-time clustering for underwater acoustic communications IEEE J Oceanic Eng (2018) M. Stojanovic et al. Underwater acoustic communica...
Then, to identify clustering noises (e.g., false positives) with the bound on the snapshots' spatiotemporal dependencies, a GCN-based soft-denoising module is conducted based on the fine- and coarse-grained Re-ID clusters. Additionally, we harness strong semantic information extracted from the ...
Noise clusteringFactorization machine (FM) is a promising model-based algorithm for collaborative filtering (CF), but can bring inferior performances if datasets include users having low confidence. In this paper, a robust FM model is proposed by introducing the noise clustering-based noise rejection...
The proposed VAD method is based on a soft-decision clustering approach built over a ratio of subband energies that improves recognition performance in noisy environments. The accuracy of the FCM-VAD algorithm lies in the use of a decision function defined over a multiple-observation (MO) window...
This paper presents the development of a novel interval-valued type-2 robust fuzzy c-regression model (IVT2RFCRM) clustering algorithm for identification of nonlinear systems taking into account the presence of noise and outliers in the associated dataset. On the one hand...
Noise clusteringCredibility functionThis paper addresses the effectiveness of fuzzy c-regression models algorithm and Euclidean particle swarm optimization to nonlinear system identification in a noisy environment. The fuzzy c-regression models (FCRM) clustering algorithm is sensitive to initialization that ...