6 results about "Kernel clustering" patented technology

The invention discloses a later fusion multi-kernel clustering method and system based on tensor learning and active reconstruction, and relates to the technical field of information clustering, and the method comprises the steps: firstly obtaining a target data sample and a clustering task, mapping the target data sample to a Hilbert space, and generating a multi-kernel matrix; carrying out active reconstruction on a base division matrix in later fusion multi-kernel clustering; applying low-rank tensor constraint to the actively reconstructed basis division matrix; establishing an objective function of a later fusion multi-kernel clustering method; performing initialization operation on all variables; by utilizing an alternating direction multiplier method, updating and optimizing a certain variable in sequence by fixing other variables; the convergence threshold value of the target function is limited, and when the change fluctuation of the target function value is smaller than the threshold value, the optimization process is stopped; and taking the directly learned consistency division matrix as input, and obtaining sample label information and a clustering result through a K-means clustering algorithm. The clustering scheme provided by the invention has remarkable effectiveness and high efficiency.

The application discloses a multi-scale structure preserving optimal transmission depth kernel clustering method, and belongs to the technical field of unsupervised machine learning, pattern recognition and data mining, and comprises the following steps: S1, multi-layer coding is performed on input unlabeled single-view data to obtain hierarchical hidden representation; S2, each layer of normalized kernel matrix is obtained based on the step S1; S3, a fusion kernel matrix is obtained based on the step S2; S4, the fusion kernel matrix obtained based on the step S2; S5, a multi-scale structure preserving regularization term of the step S3 is taken as a core constraint; and S6, after the final fusion kernel matrix is obtained after model training convergence, a multi-scale structure preserving regularization is constructed from the local neighborhood distribution consistency and the global geometric correlation, the geometric deviation of the kernel space relative to the input space is explicitly constrained, and the excessive geometric distortion and structure degradation in the kernel learning process are inhibited.

The invention discloses a method and system for identifying various foreign matters based on deep learning, and relates to the technical field of image identification, and the method comprises the steps: conveying a material through a conveyor belt, and dynamically adjusting the illumination gear according to the speed of the conveyor belt in an image collection process, so as to enable the same region to obtain an image sample under a multi-illumination condition; performing feature identification on each frame of foreign matter by using a feature extraction network, dividing feature subsets according to illumination gears, and identifying samples with incomplete features through consistency discrimination; an open type authigenic nucleus clustering structure is adopted, and a primary nucleus formed by a standard sample and an authigenic nucleus formed by a sample with incomplete characteristics jointly participate in clustering; performing category inference on the authigenic nucleus through the radiation effect of the native nucleus on the feature dimension and multi-scale pyramid generality analysis; and finally, obtaining a final recognition result of the foreign matter based on multi-frame feature weighted fusion. According to the method, high-robustness foreign matter recognition under multi-illumination, multi-scale and multi-frame fusion is realized.

The invention discloses a program code optimization method and device in a link stage, equipment and a storage medium, and the method comprises the steps: traversing each target file input into a linker, and obtaining a program section in each target file; if the program section is a clustering base point function, creating a kernel clustering instance corresponding to the clustering base point function, and executing clustering optimization operation on the clustering base point function according to the kernel clustering instance; and after the clustering base point functions in all the target files complete the clustering optimization operation, executing a linking operation on each target file. According to the technical scheme, the feasibility of the code optimization method can be improved, the influence of instruction cache deletion is eliminated, and the performance of an executable program is greatly improved.

The invention discloses a cross-scale space target detection method and a cross-scale space target detection device. The method comprises the following steps: sequentially processing an original image through a plurality of convolutional layers of an enhanced kernel clustering initial network, extracting multi-scale features and context features, extracting a relationship among long-distance pixels, and outputting a multi-level feature map; inputting the input feature X and the input feature Y into an enhanced feature fusion module of a feature pyramid network through the feature pyramid network; extracting channel enhancement features and spatial enhancement features of the input feature X and the input feature Y, and performing weighted fusion to obtain a target fusion feature map; and sending the target fusion feature map into a detector, carrying out interaction on the object query and the target fusion feature map, and predicting to obtain an object category and bounding box coordinates. According to the scheme, the detection capability of a target with severe size change in a space environment is remarkably improved, and geometric deformation of satellite components caused by view angle change can be better processed. The detection process is simplified while the performance is improved.

The invention discloses an electricity consumption anomaly detection method of a fusion model, and belongs to the technical field of power electronic control. The method mainly comprises the following steps: collecting historical power consumption data of a user, and preprocessing original data; the number of abnormal samples is increased through an oversampling technology SMOTE algorithm; dividing the processed load data into a training set and a test set to train a CNN-BiLSTM fusion model; optimizing the fuzzy kernel clustering algorithm by using a particle swarm optimization algorithm, and extracting a typical power consumption curve of normal power consumption data of the user; performing similarity coefficient calculation on the to-be-detected data and the typical power consumption curve; and judging whether the data of which the similarity is less than 0.5 belongs to power consumption abnormity or not by using a CNN-BiLSTM fusion model. And then an AT instruction can be sent to the communication module by using a computer end, so that an alarm short message can be sent to a user who is suspected to use electricity abnormally. The method can timely find and give an alarm when the user has a large difference with the past electricity consumption behavior, guarantees the safety of the user from the perspective of electricity consumption, and has a certain practical value.