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3 results about "Factor matrix" patented technology

Image classification method based on heterogeneous tensor decomposition

ActiveCN110222213AImprove classification accuracyOptimize classifierCharacter and pattern recognitionStill image data clustering/classificationData setTensor decomposition
The invention discloses an image classification method based on heterogeneous tensor decomposition. The method comprises the following steps: constructing an original tensor according to an ORL data set; decomposing the original tensor into a factor matrix and a sample feature representation matrix corresponding to each order of the tensor; adding column-by-column orthogonal constraints on the factor matrix; low-rank constraint is applied to the sample feature representation matrix, and the lowest-rank representation is obtained by identifying the global low-rank structure of the sample; through l2, 1 norm constraint loss function and a regularization item, a target of robust feature selection discrimination is realized; obtaining a complete objective function; carrying out optimization iteration on the objective function by using an alternating direction multiplier to obtain an optimal solution, and further obtaining a classifier; and inputting the test set pictures in the ORL into the trained classifier to complete image classification. The method solves the problems that in a traditional vector or matrix method, structural information is likely to be lost, and dependence among elements and heterogeneity in the tensor decomposition process are damaged.
Owner:TIANJIN UNIV

Client, server, and client-server system adapted for updating a client-item matrix

ActiveCN111492392AEnsemble learningDesign optimisation/simulationTheoretical computer scienceNative client
A client (i) adapted for updating individual elements (rij) of a client-item matrix (R) by means of Collaborative Filtering, R = XTY. The matrix (R) comprises a plurality of individual elements (rij),each individual element relating to a specific client (i) and a specific item (j). The client (i) utilizes an individual part (Ai) of a first model component (A) and at least one element of local client data (ui). The first model component (A) is a factor matrix A = X(i, k) comprising a plurality of client factor vectors (xi). The individual part (Ai) of the first model component (A) is the client factor vector (xi) for said client (i). The client (i) is connected to a server utilizing a second model component (B) and a global set of items (j1,..., jM). The second model component (B) is a factor matrix B = Y(J, k) comprising a plurality of item factor vectors (yj). The client (i) is configured to assign the individual part (Ai) of the first model component (A) to the client (i) and download a second model component (B) from the server. Thereafter, an updated individual part (Ai2) of the first model component (A) is calculated, as well as an individual value for each item (j1,..., jM).An evaluation of the value is uploaded to the server such that an updated second model component (B2) is calculated by the server. The updated second model component (B2) is downloaded from the server, and a new updated individual part (Ai3) of the first model component (A) is calculated.
Owner:HUAWEI TECH CO LTD
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