35 results about "Tensor factorization" patented technology

A method of predicting a response relationship between elements of two sets includes: specifying a dyadic response matrix; specifying covariates that measure additional dyadic relationships; specifying a number of row clusters and a number of column clusters for clustering the rows and columns of the response matrix; specifying a rank for cluster factors that model average interactions between row clusters and column clusters by products of cluster factors; and determining prediction parameters for predicting responses between elements of the first set and the second set by improving a likelihood value that relates the prediction parameters to the response matrix, the covariates, the observation weights, the row clusters and the column clusters. Determining the prediction parameters includes: updating the prediction parameters for fixed assignments of row clusters and column clusters, and updating assignments for row clusters and column clusters for fixed prediction parameters.

The invention discloses a label recommendation method and device and a readable medium, and relates to the field of label recommendation. The method comprises the steps of obtaining a target account and a target resource; determining a recommendation value of a label through a label recommendation model decomposed by the tensor, the label recommendation model being composed into a core tensor anda factor matrix by the tensor, a target sub tensor in n sub tensors of the core tensor being correspondingly multiplied by the factor matrix, and the other sub tensors being 0; and determining n labels with the highest recommendation value in the label data as labels recommended to the target account. In the process of recommending the labels through the label recommendation model, the element inother sub tensors except the target sub tensor in the core tensor is 0, namely in the equality relationship, the part participating in the equality relationship in the core tensor only comprises thepart corresponding to the target sub tensor, and the rest part does not participate in the equality relationship, so that the problem of overhigh time complexity caused by the complete three-dimensional core tensor is avoided.

An exemplary system, method and computer-accessible medium for determining a plurality of tissue parameters of a tissue(s), can include, for example, receiving information related to a plurality of rotational invariants contained within a diffusion magnetic resonance (dMR) image(s) of the tissue(s), and generating the tissue parameters using a set of rotational invariants related to the plurality of tissue parameters using such information. The tissue parameters can be generated by factorizing a response of an individual fiber segment of the tissue(s) based on the set of rotational invariants. The response of the individual fiber segments can be factorized from an orientational distribution function (“ODF”). The individual fiber segments can be factorized using a scalar tensor factorization(s) of the rotational invariants. The set of rotational invariants can be of a rotation group SO(3).

The invention provides a high-speed rail rail damage classification method, the main idea of ​​which is: firstly, the time domain and frequency domain local features of the lossy signal are extracted by wavelet analysis method, and a three-dimensional tensor signal is established for the same measurement point combined with different carriages, and the data Extending to multi-dimensional space to obtain non-negative tensors, using the alternating least squares algorithm as the iterative criterion for non-negative tensor decomposition, and then introducing singular value decomposition to improve the initialization of non-negative tensors, using the improved non-negative tensor decomposition method The hidden features are extracted, and finally the extreme learning machine algorithm is introduced to realize the real-time classification of rail damage. The method of the invention can accurately classify rail damage signals, improves the speed and accuracy of damage classification and has better robustness. The rail damage classification method proposed by the invention is superior to existing methods, can obtain better recognition effect, and can be widely used in the field of rail damage classification.

The invention discloses a multi-view face synthesis method based on tensor resolution and Delaunay triangulation, and mainly solves the problem of difficulty in face image synthesis under continuous changing views in the prior art. The method comprises the following steps of: extracting outline information of a face image by a characteristic point labeled method; separating out a view coefficientmatrix of human face data in a training set by a tensor resolution method; fitting sample bands of the view coefficient matrix; building characteristic points of a new view by adopting a tensor resolution formula; synthesizing the face image of the new view by adopting the Delaunay triangulation and linear affine transformation according to the known face image. The method has the advantages of authentic face synthesis result, wide synthesis view ranges and low operation complexity and can be applied to the field of computer vision or face synthesis under different views in a multimedia technology.

The present teaching relates to recommending content items to a user based on tensor factorization. In one example, a request is received for recommending content items to the user. Tensor data related to a plurality of users and a plurality of content items are obtained based on the request. The tensor data is decomposed into a plurality of sub-tensors based on a prior probability distribution. At least one bound is determined for a tensor factorization model that is generated based on the prior probability distribution. One or more items interesting to the user are predicted based on the at least one bound and the plurality of sub-tensors. At least one of the one or more items is recommended to the user as a response to the request.

The invention discloses an objective quality evaluation method for high dynamic range images without reference based on tensor space, which starts from the image data itself, uses a third-order tensor to represent a color high dynamic range image, and then uses Tucker in tensor decomposition The decomposition algorithm performs tensor decomposition on the distorted high dynamic range image to obtain three feature images, namely the first feature image, the second feature image and the third feature image, and the colors of the high dynamic range image are integrated in the three feature images Information, then extract the manifold structure features of the first feature image and the perceptual detail contrast features of the second and third feature images, and then use the support vector regression method in machine learning to calculate the objective quality evaluation value of the distorted high dynamic range image, so that The objective evaluation of the quality of the color high dynamic range image without reference is realized, and the evaluation effect is obviously improved, thereby effectively improving the correlation between the objective evaluation result and the subjective perception.

The invention discloses a pre-stack seismic reflection mode analysis method based on a tensor discriminant dictionary, comprising the following steps: S1, acquiring pre-stack seismic signals; S2, adding neighborhood gather information of target gathers, and target gather data Composing multi-dimensional pre-stack seismic data together; S3, making training data; S4, constructing the objective function, and formulating a classification strategy that is conducive to the classification of pre-stack seismic data on the basis of the discriminant tensor dictionary learning algorithm; S5, iteratively updating the model; S6. Put the pre-stack seismic data into the model trained in step S6, and obtain the reflection mode result of the target layer in the whole work area. The invention is based on Tucker tensor decomposition, can process multi-dimensional data well, better utilize multi-dimensional information of pre-stack seismic data, can have higher classification accuracy for pre-stack seismic reflection mode analysis, and is robust to noise better.