6 results about "Biclustering" patented technology

The invention discloses a convex biclustering method based on robust statistics in the technical field of pattern recognition. The convex biclustering method comprises the following steps: S1, inputting data information into a model; s2, the ADMM model is optimized; s3, performing iterative optimization and convergence judgment; s4, generating a difference matrix; s5, calculating and outputting a clustering result according to label distribution; according to the method, robust statistics are introduced into a convex biclustering objective function, so that the influence of abnormal data on a clustering result is effectively weakened; the problem of distortion of clustering results caused by abnormal values is fundamentally relieved, the practical application range of the convex biclustering method in high-noise, large-scale and strong-interference environments is expanded, and the convex biclustering method has important theoretical value and practical significance; through reasonable decomposition and an ADMM optimization framework, a common local optimal trap problem in a traditional non-convex biclustering method is avoided, and an optimal solution can be stably converged; and the accuracy and reliability of the clustering result are ensured.

The application discloses a high-frequency oscillation rhythm detection method and system based on an adaptive fuzzy double clustering algorithm, and relates to the medical treatment technical field. The particle swarm optimization algorithm is a kind of random optimization algorithm based on group, has high requirements for the initial position of the solution, has strong global search capability, and is not easy to fall into local optimal value, and has strong processing capability for complex samples with large quantity, so that the particle swarm optimization algorithm is used to optimize the fuzzy double clustering algorithm, and the optimized clustering center can be obtained, and the high-frequency oscillation rhythm of the epilepsy electroencephalogram signal can be detected based on the optimized clustering center. The application can improve the detection precision of the high-frequency oscillation rhythm of the epilepsy electroencephalogram signal, and help doctors to diagnose epilepsy and remove the epileptogenic focus.

The invention provides a gene expression profile bi-clustering method based on a swarm intelligence algorithm, and belongs to the technical field of gene expression, and the method comprises the following steps: constructing an index matrix for a gene expression data matrix, obtaining a bi-clustering seed, initializing a population, taking the bi-clustering seed as a first population individual, and constructing a second population individual; adding the first population individuals into a second population for population expansion to obtain two population individuals, evaluating the first population individuals by using a fitness function, and evaluating a plurality of individuals which contribute to the second population to the greatest extent by the individuals in the first population until a preset maximum number of evolution times is reached; and selecting an optimal bi-cluster from the second population. The index matrix is constructed for the gene expression data matrix, so that later data processing is simpler and higher in efficiency, meanwhile, data values of each gene under each condition can be searched by using the swarm intelligence algorithm, and therefore, the search range is wide, and important biological information is not easy to miss.

The invention discloses a microscopic image core semantic text generation method in a bi-clustering process, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring a general semantic set through a large language model, and processing microscopic images to obtain a microscopic target image set of each category; preprocessing the text and the image, inputting the preprocessed text and image into a trained image-text model, generating text and image representation vectors, and constructing a category-semantic similarity matrix through cosine similarity calculation; extracting different similar sets from the matrix, and respectively performing intra-class and inter-class clustering to obtain generalization and discrimination semantic text sets of all classes; and taking an intersection of the two types of sets to obtain a core semantic text. According to the method, accurate screening of microscopic image semantics is realized through a biclustering strategy, the generated core semantic text has generalization and discrimination, the semantic alignment quality is improved, and high-quality semantic data support can be provided for fine tuning of a multi-modal model.

This invention discloses a method for generating core semantic text from microscopic images using a dual-clustering process, belonging to the field of medical image processing technology. The invention obtains a general semantic set through a large language model, processes the microscopic images to obtain sets of microscopic target images for each category, and then inputs these preprocessed sets into a trained image-text model to generate text and image representation vectors. A category-semantic similarity matrix is ​​constructed using cosine similarity calculation. Different similarity sets are extracted from the matrix, and intra- and inter-class clustering is performed to obtain generalized and discriminative semantic text sets for each category. The intersection of the two sets yields the core semantic text. This invention achieves accurate semantic filtering of microscopic images through a dual-clustering strategy, and the generated core semantic text possesses both generalization and discriminative properties, improving semantic alignment quality and providing high-quality semantic data support for fine-tuning multimodal models.

The invention discloses an underwater target tracking classification method and system. The tracking classification method comprises the following steps: S3, carrying out ROI region identification, and identifying a potential target region by using an FCM and K-means biclustering algorithm; s4, generating a high-overlapping-degree ROI mask, and calculating the intersection-to-union ratio of an FCM result and a K-means result; s5, target segmentation is carried out, and target segmentation is realized in combination with FCM pre-segmentation and a PCNN neural network; s6, performing target detection, analyzing and detecting the segmented target based on a connected region, and extracting static features of the target; s7, multi-target tracking is carried out, and target tracking is realized based on an improved SORT algorithm; and S8, target intelligent classification is carried out, static features and dynamic features of the target are fused, and classification and identification of the underwater target are realized. According to the tracking classification method and system, accurate detection, stable tracking and automatic classification of underwater biological targets can be realized.