49 results about "Manifold regularization" patented technology

The invention discloses a semi-supervision sequencing study method for image searching based on manifold regularization. The semi-supervision sequencing study method comprises the following steps of: extracting visual characteristics from a network searching result from a database or an initial text-based network to form an image sample set; dividing the image sample set into three grades including 2, 1, and 0 according to degrees for inquiring subject coherence, wherein 2 represents that the image sample set is very coherent with the inquiry, 1 represents that the image sample set is commonly coherent with the inquiry and 0 represents that the image sample set is incoherent with the inquiry; calculating spurious correlation grade information yi of an unmarked image sample; calculating a distance between two image samples; constructing a Laplace manifold regularization item according to the distance between the two image samples; constructing a target function through the Laplace manifold regularization item; and solving a sequencing score for obtaining each image sample by the target function and feeding a sequenced result back to a user. According to the semi-supervision sequencing study method disclosed by the invention, the searching and sequencing performances are improved, marking information is sufficiently utilized and the searching precision is improved; and less supervision information is effectively utilized to improve the sequencing performance.

The invention discloses a robust visual image classification method and system. In order to effectively realize the category prediction of unlabeled samples in training samples and the rapid induction and reasonable dimensionality reduction of samples to be tested, the error measurement based on elastic regression analysis is integrated into Train an out-of-sample label propagation model, and normalize the manifold regularization term through parameter trade-offs, based on soft labels l 2,1 Norm regularized label fitting term and based on l 2,1 The impact of the norm regularized elastic regression residual term on the sample description and category identification completes the establishment of the label propagation model; and then obtains the projection matrix used to determine the category of the sample to be tested through the iterative optimization of the label propagation model. Therefore, in this application by introducing based on l 2,1 Norm regularized regression error term and soft label l 2,1 Norm regularization can effectively improve the robustness of the system while inheriting the advantages of the label propagation classification method, making the induction process of the samples to be tested fast and accurate.

The invention discloses a domain transfer extreme learning machine method based manifold regularization and norm regularization. On the basis of a traditional extreme learning machine, the thought of semi-supervised learning and transfer learning is introduced, and a novel extreme learning machine model is built and consists of three parts: a manifold regularization term capable of excavating geometric distribution shapes of data samples with tags and without tags to realize semi-supervised learning; a loss function term considering error minimization of source domain data and target domain data to realize transfer learning; and norm regularizers constraining weight space. The domain transfer extreme learning machine method provide by the invention is combined with the source domain to process the problem of prediction of the target domain, thereby increasing the generalization capability and range of application of the extreme learning machine. Introduction of the manifold regularization term also enables the method proposed by the invention to still maintain a relatively good learning effect when data with tags are little, the restriction that a traditional machine learning method requires a large amount of data with tags is overcome, and the accuracy and robustness of prediction are also improved.