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Found 22 papers, 9 papers with code
Adversarial Deep Network Embedding for Cross-network Node Classification
This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.
Network Together: Node Classification via Cross-Network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Network Together: Node Classification via Cross network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Social and Information Networks
Multi-view Information-theoretic Co-clustering for Co-occurrence Data
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.
Domain-adaptive Message Passing Graph Neural Network
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently.
Dual Representation Learning for One-Step Clustering of Multi-View Data
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.
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces.
Transfer Prototype-based Fuzzy Clustering
Specifically, the idea of leveraging knowledge from the source domain is exploited to develop a set of transfer prototype based fuzzy clustering (TPFC) algorithms.
Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
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.
Joint Information Preservation for Heterogeneous Domain Adaptation
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).
Deep Image Feature Learning with Fuzzy Rules
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.
Multi-view Clustering with the Cooperation of Visible and Hidden Views
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.
Double-Coupling Learning for Multi-Task Data Stream Classification
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream.
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images
Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose.
TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification
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.
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection
In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy.
Graph Fuzzy System: Concepts, Models and Algorithms
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.
A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance
Model transparency, label correlation learning and the robust-ness to label noise are crucial for multilabel learning.
Cooperation Learning Enhanced Colonic Polyp Segmentation Based on Transformer-CNN Fusion
Secondly, a fusion module is designed to integrate the features from two branches.
Multi-view Fuzzy Representation Learning with Rules based Model
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 Takagi-Sugeno-Kang Fuzzy System
Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels.
