9 results about "Label propagation" patented technology

The application discloses a noise label robust training method and device for an image recognition model, and belongs to the technical field of image recognition model training, and comprises the following steps: inputting image training samples into a deep neural network for feature extraction; constructing a multi-granularity granular ball structure in a feature space based on a feature vector; performing hierarchical correction on the labels of the image training samples; performing label propagation in the granular ball, and calculating the propagation confidence distribution and consistency score of each image sample; screening a clean sample subset according to the consistency score, and iteratively training the deep neural network to update network parameters. Through the adaptive multi-granularity granular ball division mechanism, the application can fully depict the local structure characteristics of data in the feature space, avoid the problems of excessive fragmentation or insufficient purity caused by traditional fixed-granularity clustering in a high-noise environment, and provide a stable and reliable structure prior for subsequent label correction and information propagation.

The application provides a kind of telecommunication anomaly detection method and detection device.The method comprises: obtaining the social relationship information and portrait feature information of each user in personnel relationship heterogeneous graph, obtaining the fusion feature information and relationship feature information of each user according to label information, portrait feature information and social relationship information;According to fusion feature information and relationship feature information, adopt neighborhood aggregation algorithm, obtain the node feature information of label user;Based on semi-supervised learning mechanism, according to social relationship information and node feature information, through improved attention mechanism model, obtain the anomaly probability of each user, to realize through label propagation algorithm, the label information of label user is propagated to unlabelled user, and anomaly detection is completed.The method of the application utilizes the context information of multiple relationships in heterogeneous graph and the characteristics of semi-supervised learning, effectively improves the detection accuracy and adaptability of the model in the label scarce scene.

The application discloses a data processing method and related device, obtains a training sample matrix composed of labeled image samples of a source domain and unlabeled image samples of a target domain, a sample label of the labeled image samples is used for identifying category information of the labeled image samples, an initial field alignment matrix, an initial global similarity matrix and an initial predicted label matrix are updated according to the training sample matrix, a target alignment matrix, a target global similarity matrix and a target predicted label matrix are obtained, a check parameter is constructed according to the training sample matrix, the target alignment matrix, the target global similarity matrix, the target predicted label matrix and a source domain sample label matrix, if the check parameter does not satisfy a first convergence condition, iterative updating is performed until the first convergence condition is satisfied, and it is considered that training of an image recognition model of the target domain is completed. Label propagation can make the labeled image samples of the source domain be used in image recognition of the target domain, and improve training efficiency of the image recognition model of the target domain.

This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provides a method for learning robust sensor-specific autoencoder based fusion model by utilizing a graph structure to perform label propagation and correction. In the disclosed Graph regularized AutoFuse (GAF) method latent representation for each sensor is learnt using the sensor-specific autoencoders. Further these latent representations are combined and fed to a classifier for multi-class classification. The disclosure presents a joint optimization formulation for multi-sensor fusion where label propagation and correction, sensor-specific learning and classification are executed together.

This invention relates to the fields of graph neural networks and heterogeneous graph representation learning technology, specifically to a heterogeneous graph node classification method based on relation-aware label propagation. The method includes the following steps: first, based on a given heterogeneous network, then performing type-specific linear transformations on nodes of different types and projecting their features onto a common feature space; second, designing a relation-aware label propagation algorithm for target nodes in the heterogeneous network, generating pseudo-labels through relation subgraphs; and third, employing a two-layer aggregation strategy based on type attention to transmit information and effectively fuse multi-level information of nodes, resulting in richer and more accurate node feature representations. This invention significantly improves classification performance and robustness by designing a relation-aware label propagation method to obtain pseudo-labels, utilizing two-layer aggregation based on type attention for heterogeneous message transmission, and combining it with a multi-objective optimization strategy.

The application provides a microbe-disease correlation prediction method based on multi-order similarity fusion learning, comprising the following steps: S1: obtaining a microbe function similarity matrix P M,1 and a disease function similarity matrix P D,1 ; S2: obtaining a microbe-disease correlation matrix Y, and calculating a microbe linear neighborhood similarity matrix P M,2 and a cosine similarity matrix P M,3 , and meanwhile, calculating a disease linear neighborhood similarity matrix P D,2 and a cosine similarity matrix P D,3 ; S3: constructing a multi-order similarity fusion learning method; S4: finally, adopting a label propagation method to obtain a final prediction result. The application fuses multiple similarities by constructing a multi-order similarity fusion learning method, and effectively utilizes the constructed similarity network by combining the label propagation, so that the correlation between microbes and diseases can be accurately predicted.

The disclosure discloses a reported classification optimization method and device, electronic equipment and storage medium. Through the present application, the training data set is expanded by using a small amount of labeled data through a label propagation technology, which is suitable for the characteristics of the scarcity of industrial text data labeling. At the same time, the classification accuracy is improved through model parameter optimization and cross-validation, and the model performance is continuously optimized through the incremental learning mechanism combining confidence judgment and manual correction. Therefore, the technical problem that the professional term recognition rate is reduced and the class boundary is blurred due to the use of a traditional SVM model and the non-optimization for the speciality of industrial text in the existing text classification method, thereby affecting the timeliness of risk early warning, can be solved. The technical effects of improving the accuracy of industrial event report text classification and professional term recognition ability, clarifying the class boundary, and dynamically optimizing the model are achieved, thereby ensuring the timeliness of risk early warning and strengthening the efficiency of industrial safety management.

This invention relates to the fields of complex network analysis and social network data mining, and provides a community discovery method based on user values, comprising the following steps: S1: constructing user-topic dual-encoding representations; S2: decoupling representations based on value hierarchy structure; S3: value semantic alignment constraints; S4: user value representation generation and optimization; S5: hybrid similarity construction and node centrality calculation; S6: label update strategy based on hybrid centrality ranking; S7: label propagation driven by multi-level influence; S8: community attribute center construction and fuzzy node identification; S9: fuzzy node re-attribution correction based on community attribute centers to solve the problems of difficulty in obtaining stable user value representations under low / no corpus conditions, and difficulty in effectively introducing value orientation as node attributes into community partitioning, thereby achieving stable community partitioning based on user values.