Second, a new regularization method based on L_(2, 1)-norm regression is proposed to mine the consistency information between views, while the geometric structure of the data is preserved through the Laplacian graph.
Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels.
Secondly, a fusion module is designed to integrate the features from two branches.
Model transparency, label correlation learning and the robust-ness to label noise are crucial for multilabel learning.
Finally, a learning framework of GFS is proposed, in which a kernel K-prototype graph clustering (K2PGC) is proposed to develop the construction algorithm for the GFS antecedent generation, and then based on graph neural network (GNNs), consequent parameters learning algorithm is proposed for GFS.
Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole.
In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy.
The proposed method has the following distinctive characteristics: 1) it can deal with the incomplete and few labeled multi-view data simultaneously; 2) it integrates the missing view imputation and model learning as a single process, which is more efficient than the traditional two-step strategy; 3) attributed to the interpretable fuzzy inference rules, this method is more interpretable.
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream.
How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge.
The existing algorithms usually focus on the cooperation of different views in the original space but neglect the influence of the hidden information among these different visible views, or they only consider the hidden information between the views.
The method progressively learns image features through a layer-by-layer manner based on fuzzy rules, so the feature learning process can be better explained by the generated rules.
More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension.
The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA).
Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS.
A core issue in transfer learning is to learn a shared feature space in where the distributions of the data from two domains are matched.
Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis.
Specifically, the idea of leveraging knowledge from the source domain is exploited to develop a set of transfer prototype based fuzzy clustering (TPFC) algorithms.
Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces.