39 results about "Higher order tensor" patented technology

Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

The invention relates to the technical field of medical instruments, provides a novel practical method for extracting MRI (magnetic resonance imaging) parameters of structural abnormality of white matters of a human brain, and aims to achieve the purpose of objectively evaluate the lesion degree by analyzing anisotropy of tissues through a certain characteristic extraction method. The invention adopts the technical scheme that the method for extracting high-order tensor characteristic parameters of diffusion kurtosis tensor imaging comprises the following steps: a subject collects diffusion weighted signals of tissues along a plurality of directions on a magnetic resonance scanner, then a second-order or fourth-order tensor that reflects diffusion and distribution probability density function of water molecules in the tissues can be obtained through fitting, and lesion characteristics of diffusion and distribution abnormality of the water molecules, caused by structural abnormality of the tissues, can be obtained through certain tensor analysis, wherein parameters from two different angles, including diffusion coefficient and kurtosis coefficient, are mainly included. The invention is mainly applied to design and manufacture of medical instruments.

The invention discloses a three-dimensional heart magnetic resonance imaging method based on tensor composition sparse bound. According to the method, undersampling of whole heart three-dimensional K spatial data is achieved through a three-dimensional radial sampling track; sparse representation of a magnetic resonance image is achieved through high-order tensor decomposition, optimal sparse representation of whole heart three-dimensional magnetic resonance data is achieved, and magnetic resonance imaging accuracy is improved; L1 norms of tensor decomposition sparse data and composite regularization terms are combined to serve as bound terms, reconstruction of the undersampled three-dimensional heart magnetic resonance image is achieved through a rapid composite splitting algorithm. By the adoption of the method, magnetic resonance imaging scanning time is effectively shortened, the heart magnetic resonance imaging speed is increased, motion artifacts generated in the heart detection process can be conveniently eliminated, and three-dimensional heart magnetic resonance imaging accuracy is improved.

The present invention discloses a data cleaning method. The method comprises: acquiring to-be-cleaned data, and according to analysis for noise data distribution in the to-be-cleaned data, obtaining to-be-cleaned fields in the to-be-cleaned data; searching a field which can be subjected to dimension expansion in the to-be-cleaned data, and performing high-order tensor dimension-expanding on the field which can be subjected to dimension expansion to obtain M tensor fieldsets; and carrying out data cleaning on the to-be-cleaned fields by using tensor fields in the tensor fieldsets, which are associated with the to-be-cleaned fields. The present invention also discloses a data cleaning apparatus.

The invention discloses a channel state information positioning fingerprint construction method based on tensor decomposition. First, the collected original channel state information (Channel State Information, CSI) data is represented as a three-dimensional image; then the three-dimensional image is regarded as a third-order tensor; then the analysis model based on Parallel Factor (PARAFAC) The tensor decomposition algorithm combined with the Alternate Least Squares (ALS) iterative algorithm is used for tensor noise reduction processing; then, the tensor wavelet decomposition algorithm is used to perform a single-layer tensor on the three dimensions of the CSI image Quantitative wavelet decomposition, and the use of angular second-order moments to calculate the wavelet coefficients of each wavelet subcomponent; finally, the CSI positioning fingerprints corresponding to the coordinates of each reference point are obtained. The present invention makes full use of the characteristics that high-order tensors can describe data information and structures, expresses complex data in the form of tensors, finally realizes noise reduction and feature extraction of tensor images, and improves the ability of data processing and analysis.

The present invention discloses a millimeter wave system channel feedback method based on tensor parallel compression. The terminal uses tensor parallel compression technology to compress the channel state information tensor into several tensors of the same size; 3. The receiver uses CANDECOMP / PARAFAC (CP) decomposition to decompose the above compressed tensors respectively, and obtains The factor matrix is ​​fed back to the transmitting end through the uplink feedback link; 4. The transmitting end restores the factor matrix of the original channel state information tensor according to the factor matrix currently fed back to the transmitting end, and reproduces the factor matrix obtained by restoring To construct the original channel state information, the present invention utilizes the high-dimensional characteristics of the millimeter-wave MIMO-OFDM channel, uses a high-order tensor model combined with tensor parallel compression technology, does not require the sparse characteristics of the downlink channel, and can effectively ensure feedback accuracy. Reduce feedback overhead.

A processing method for high-order tensor data is disclosed, which can avoid destroying the internal structure of the data during the vectorization process of the image observation sample set, is flexible and simple, can extract the discriminative features of the tensor samples, and simplifies the image observation samples Concentrating a large amount of redundant information in high-order tensor data improves the image processing speed. This processing method for high-order tensor data decomposes high-order tensor data into three parts: shared subspace components, individual subspace components, and noise parts; Quantitative data is expressed as a linear combination of a set of tensor bases and vector coefficients; the variational EM method is used to solve the base tensor and vector coefficients; a classifier is designed to classify the samples to be tested by comparing the marginal distribution of samples.

The invention discloses a multi-attribute volume data compression method based on high-order tensor approximation. It includes preprocessing multi-attribute volume data, decomposing high-order tensors of block data into factor matrices and core tensors, and obtaining approximate high-order tensors according to the reconstruction of factor matrices and core tensors. For each The attribute volume is fused and drawn to complete the multi-attribute volume data compression. The invention greatly reduces the compression rate of the tensor approximation, and at the same time retains the original data of the multi-attribute body, effectively reflecting the target characteristics of the data.