33 results about "Multiview learning" patented technology

A system and method for detecting anomalies in a communication network includes detecting first outliers in a first set of quality indicators for a cellular group, detecting second outliers in a second set of performance indicators for the cellular group, correlating the first outliers and the second outliers to produce an anomaly candidate, determining a confidence threshold for the anomaly candidate, and indicating a network anomaly in response to the confidence threshold exceeding a predetermined threshold.

The invention discloses a color face recognition method based on semi-supervised multi-view dictionary learning. The method applies multi-view learning technology to dictionary learning of semi-supervised color face images. In the training phase, the method learns the structured dictionaries of each color component separately and makes these dictionaries orthogonal to each other to remove the correlation between each color component, making full use of the complementary color difference information between each color component; and the method In the dictionary learning process, color face image samples without class labels are used to participate in the training, and the information of all training samples is fully utilized. In the classification test stage, the method accumulates the reconstruction errors of each color component and uses the dictionary corresponding to each type of training samples to reconstruct the test samples, and finally classifies the test samples into the class with the smallest cumulative reconstruction error. The recognition effect of the invention is higher, and the color face recognition ability is obviously enhanced through the semi-supervised multi-view dictionary learning.

The invention discloses a short-term traffic forecasting method based on a multi-task multi-view learning model. The method comprises the following steps: 1, constructing a space-time data model individually for each road segment; 2, constructing a multi-core learning model; 3, constructing an objective function by adopting a multi-task multi-view feature learning model; 4, introducing a particleswarm optimization algorithm to optimize the objective function obtained in the step 3; and 5, repeating the steps 1 and 2 for any road segment to obtain input features, bringing the input features into an optimized objective function, and realizing short-term traffic prediction for any road segment. The method realizes the efficient prediction of short-term traffic, solves the problem that the spatial-temporal heterogeneity and the global prediction ability of the model can not reach equilibrium, solves the problem of parameter optimization of the model, and can be widely used in urban planning, human mobility survey, automobile navigation, emergency response, space-time accessibility analysis and traffic pollution modeling.

The present invention relates to the technical field of the Internet, and more specifically, relates to a webpage classification method for semi-supervised multi-view learning, comprising: obtaining data from webpages and establishing a training set; training a classifier through a marked training set; The machine codes the marked training set and the unmarked training set to obtain the sample features; performs density clustering on the sample features to obtain the clustering results; and classifies the samples of the unmarked training set according to the clustering results. This scheme uses the labeled training set to train the classifier, adds orthogonal constraints and confrontational similarity constraints on the basis of the existing multi-view classification method, and then uses the trained classifier to perform density aggregation on all the data in the training set. Finally, the accuracy of the classifier is verified, and the above-mentioned process can be iterated multiple times to improve the classification performance of the classifier.

The invention relates to a search engine user information demand satisfaction evaluation method that combines multi-view and semi-supervised learning. The user satisfaction model of supervised learning and six stages of evaluation, the present invention uses a small amount of labeled data and a large amount of unlabeled data to improve the performance of the evaluation model through the method of semi-supervised learning, and introduces the idea of ​​multi-view learning to overcome the traditional Semi-supervised learning methods of views are prone to fall into the problem of local optima. The beneficial effects are: (1) the user satisfaction of search engine information needs can be effectively evaluated with a small amount of labeled data; (2) the user satisfaction model evaluation can be improved by using a small amount of labeled data and a large amount of unlabeled data performance; (3) Describe the user's search process from the perspective of behavior and time, and avoid the model from falling into local optimum through mutual learning.

The invention discloses a method, device and medium for complementing missing data in time and space based on multi-view learning. The method aims at massive and multi-type environmental monitoring data sets, and constructs each data missing monitoring site including monitoring site, monitoring time, etc. , monitoring parameter data tensor in three dimensions, and decompose three mixed view matrices, use the matrix complete algorithm to estimate the missing data from the local and global scales for the three mixed views constructed, and then perform multiple View learning to obtain the estimation results of the missing values ​​of the final monitoring parameters. The invention can complete the missing value of the environmental monitoring data set, and has important significance in the fields of marine environment monitoring, smart city development and the like.