31results about How to "Reduce redundant features" patented technology

The invention discloses a cross-dataset facial expression recognition model construction method and a recognition method. The reconstruction error of the training expression image is constrained by an adaptive non-negative weighting matrix, which strengthens the role of important features in image data representation and reduces the need for significant reconstruction. Useless features for errors. In addition, projecting the training set into an appropriate subspace through the mapping matrix can better reveal the intrinsic similarity between image samples across datasets, so that subspaces based on low-rank and sparse representations can learn robust image reconstructions. In order to facilitate the final cross-dataset facial expression recognition.

The present invention provides a feature selection method and device. The method includes: extracting M features from the obtained image; using a filtering model algorithm to filter the M features to obtain N features; using the first The encapsulated model algorithm searches the N features to obtain a first feature subset, and uses the second encapsulated model algorithm to search the N features to obtain a second feature subset; for the first feature Subset and the second feature subset are recombined to obtain a third feature subset including K features, and the best feature of the image is selected from the first feature subset, the second feature subset and the third feature subset Excellent feature set. Through the technical solution of the present invention, the most effective features can be selected from a large number of features, the optimal feature subset can be obtained, the computational complexity can be reduced, the classification accuracy and classification performance of face recognition can be improved, the recognition ability of faces can be improved, and the recognition ability of faces can be improved. recognition rate and improve information security.

The invention discloses an intrusion detection method based on semi-supervised learning. The implementation steps include: selecting a mixed sample set initially containing labeled samples and unlabeled samples to be tested, calculating the information gain of each feature value in the feature space and completing the process based on The feature selection of information entropy, and then screen the labeled samples based on the feature selection of information entropy, and use the new training data obtained by screening for the semi-supervised training of the classifier based on LapSVM, and use the trained classifier to be tested. The samples are classified, the best evaluation value of the detection index is determined according to the detection index, and the classification result corresponding to the evaluation value of the best detection index is output. The present invention adopts the method of feature selection to deal with the redundancy phenomenon that is easy to appear in the network environment data, and uses a small number of labeled samples and a large amount of unlabeled data to create a semi-supervised learning model, while reducing the false alarm rate and improving the detection rate, It can reduce data redundancy and improve detection efficiency.