Abstract: A face image clustering method and system based on a localized simple multiple kernel k-means is provided. The face image clustering method based on localized simple multiple kernel k-means includes the following steps: S1, acquiring face images, and preprocessing the acquired face images to obtain an average kernel matrix for each view; S2, calculating n (?×n)-nearest neighbor matrices according to the obtained average kernel matrices; S3, calculating a localized kernel matrix for each view according to the nearest neighbor matrices; S4, constructing a localized simple multiple kernel k-means clustering objective function according to the calculated localized kernel matrix for each view; S5, solving a minimum of the constructed objective function by adopting a reduced gradient descent method to obtain an optimal clustering partition matrix; and S6, performing k-means clustering on the obtained clustering partition matrix to achieve clustering.

Abstract: A later-fusion multiple kernel clustering machine learning method and system based on proxy graph improvement is provided. The method includes: S1. acquiring a clustering task and a target data sample; S2. initializing a proxy graph improvement matrix; S3. running k-means clustering and graph improvement on each view corresponding to the acquisition of the clustering task and the target data sample, and constructing an objective function by combining kernel k-means clustering and graph improvement methods; S4. cyclically solving the objective function constructed in step S3 so as to obtain a graph matrix, which is fused with basic kernel information; and S5. performing spectral clustering on the obtained graph matrix, so as to obtain a final clustering result. By means of the method, an optimized basic division not only has information of a single kernel, but can also obtain global information by means of a proxy graph.

Abstract: A spectral clustering method and system based on unified anchor and subspace learning is provided. The spectral clustering method based on unified anchor and subspace learning includes: S1: acquiring a clustering task and a target data sample; S2: performing unified anchor learning on multi-view data corresponding to the acquired clustering task and the acquired target data sample, and adaptively constructing an objective function corresponding to an anchor graph according to a learned unified anchor; S3: optimizing the constructed objective function by using an alternating optimization method to obtain an optimized unified anchor graph; and S4: performing spectral clustering on the obtained optimized unified anchor graph to obtain a final clustering result.

Abstract: A multi-view clustering method and system based on matrix decomposition and multi-partition alignment are provided.

Abstract: A citation network graph representation learning system and method based on multi-view contrastive learning is provided. The citation network graph representation learning system involved in the present application comprises: a sample construction module, which is configured to construct a corresponding negative sample based on an original graph; a graph enhancement module, which is configured to obtain a positive sample graph and a negative sample graph; a fusion module, which is configured to obtain a consensus representation of the positive sample graph and the negative sample graph by means of a cross view concentration fusion layer; a mutual information estimation module, which is configured to compare learning representations of positive sample pairs and negative sample pairs by means of a discriminator; and a hard sample mining module, which is configured to represent the consistency between the negative sample pairs according to a pre-calculated affinity vector, and select and reserve nodes.

Abstract: A late fusion multi-view clustering method and system based on local maximum alignment are provided. The late fusion multi-view clustering method based on local maximum alignment includes the following steps: S1: acquiring a clustering task and a target data sample; S2: initializing a permutation matrix of each view and a combination coefficient of each view, and performing average partition of kernel k-means clustering on an average kernel to obtain a neighbor matrix of each view; S3: calculating basic partition of each view, and establishing a late fusion multi-view clustering objective function based on maximum alignment; S4: acquiring basic partition having local information, and establishing a late fusion multi-view clustering objective function based on local maximum alignment; S5: solving the established late fusion multi-view clustering objective function based on local maximum alignment in a cyclic manner to obtain optimal partition; and S6: performing k-means clustering on the optimal partition.

Abstract: Provided are antibodies specific for the heparan sulfate binding site of Receptor for Advanced Glycation Endproducts (RAGE). Also provided are methods for treating of conditions in which RAGE is involved comprising administration of the antibodies to an individual in need of treatment.

Abstract: An aqueous binder for a lithium ion battery, a preparation method and a use thereof. The binder is an inorganic-organic composite emulsion, comprising a dispersing agent, inorganic nanoparticles, (methyl)acrylate monomers, unsaturated carboxylic acid monomers, vinyl hydrocarbon monomers and optionally copolymers of other copolymerizable monomers, wherein the dispersing agent is a water-soluble cellulose grafted amphiphilic copolymer. When the water-soluble cellulose grafted amphiphilic copolymer is used as the dispersing agent, the agglomeration of the nanoparticles when the binder is formed into a film can be avoided, and at the same time, the effects of toughening and improving the binding strength can be achieved. Meanwhile, the water-soluble cellulose has certain strengthening and toughening properties so that the aqueous binder has an excellent anti-tensile performance. The aqueous binder for a lithium ion battery can be used for lithium ion batteries.

Abstract: An aqueous binder for a lithium ion battery, a preparation method and a use thereof. The binder is an inorganic-organic composite emulsion, comprising a dispersing agent, inorganic nanoparticles, (methyl)acrylate monomers, unsaturated carboxylic acid monomers, vinyl hydrocarbon monomers and optionally copolymers of other copolymerizable monomers, wherein the dispersing agent is a water-soluble cellulose grafted amphiphilic copolymer. When the water-soluble cellulose grafted amphiphilic copolymer is used as the dispersing agent, the agglomeration of the nanoparticles when the binder is formed into a film can be avoided, and at the same time, the effects of toughening and improving the binding strength can be achieved. Meanwhile, the water-soluble cellulose has certain strengthening and toughening properties so that the aqueous binder has an excellent anti-tensile performance. The aqueous binder for a lithium ion battery can be used for lithium ion batteries.