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936 results about "Singular value decomposition" patented technology

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It is the generalization of the eigendecomposition of a positive semidefinite normal matrix (for example, a symmetric matrix with non-negative eigenvalues) to any m×n matrix via an extension of the polar decomposition. It has many useful applications in signal processing and statistics. Formally, the singular value decomposition of an m×n real or complex matrix 𝐌 is a factorization of the form 𝐔𝚺𝐕*, where 𝐔 is an m×m real or complex unitary matrix, 𝚺 is an m×n rectangular diagonal matrix with non-negative real numbers on the diagonal, and 𝐕 is an n×n real or complex unitary matrix.

Methods and apparatus related to pruning for concatenative text-to-speech synthesis

The present invention provides, among other things, automatic identification of near-redundant units in a large TTS voice table, identifying which units are distinctive enough to keep and which units are sufficiently redundant to discard. According to an aspect of the invention, pruning is treated as a clustering problem in a suitable feature space. All instances of a given unit (e.g. word or characters expressed as Unicode strings) are mapped onto the feature space, and cluster units in that space using a suitable similarity measure. Since all units in a given cluster are, by construction, closely related from the point of view of the measure used, they are suitably redundant and can be replaced by a single instance. The disclosed method can detect near-redundancy in TTS units in a completely unsupervised manner, based on an original feature extraction and clustering strategy. Each unit can be processed in parallel, and the algorithm is totally scalable, with a pruning factor determinable by a user through the near-redundancy criterion. In an exemplary implementation, a matrix-style modal analysis via Singular Value Decomposition (SVD) is performed on the matrix of the observed instances for the given word unit, resulting in each row of the matrix associated with a feature vector, which can then be clustered using an appropriate closeness measure. Pruning results by mapping each instance to the centroid of its cluster.
Owner:APPLE INC

Electroencephalogram feature extracting method based on brain function network adjacent matrix decomposition

InactiveCN102722727AIgnore the relationshipIgnore coordinationCharacter and pattern recognitionMatrix decompositionSingular value decomposition
The invention relates to an electroencephalogram feature extracting method based on brain function network adjacent matrix decomposition. The current motion image electroencephalogram signal feature extraction algorithm mostly focuses on partially activating the qualitative and quantitative analysis of brain areas, and ignores the interrelation of the bran areas and the overall coordination. In light of a brain function network, and on the basis of complex brain network theory based on atlas analysis, the method comprises the steps of: firstly, establishing the brain function network through a multi-channel motion image electroencephalogram signal, secondly, carrying out singular value decomposition on the network adjacent matrix, thirdly, identifying a group of feature parameters based on the singular value obtained by the decomposition for showing the feature vector of the electroencephalogram signal, and fourthly, inputting the feature vector into a classifier of a supporting vector machine to complete the classification and identification of various motion image tasks. The method has a wide application prospect in the identification of a motion image task in the field of brain-machine interfaces.
Owner:启东晟涵医疗科技有限公司

Clustering collaborative filtering recommendation system based on singular value decomposition algorithm

The invention provides a clustering collaborative filtering recommendation technology based on a singular value decomposition algorithm. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm comprises firstly classifying users by using user attributive character values provided by the clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm, and reducing dimension of a user-commodity grade matrix; improving a singular value decomposition (SVD) algorithm which is frequently used in image processing and natural language processing, and using the improved SVD algorithm in a recommendation system; decomposing a grade matrix in a cluster where users are located, and aggregating the decomposed grade matrix so as to fill predicted scores of non-grade items in the grade matrix, calculating similarity of the users in the same cluster by using the filled grade matrix, calculating final predicted scores of a commodity by applying collaborative filtering technologies based on the users and widely applied in the recommendation system, and carrying out final recommendation. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm has the advantages of being capable of improving recommendation efficiency of the recommendation system, solving the problems such as data sparsity of the recommendation system, and meanwhile being capable of improving accuracy rate of recommendation of the recommendation system.
Owner:BEIJING UNIV OF POSTS & TELECOMM

Multi-scale normal feature point cloud registering method

The invention relates to a multi-scale normal feature point cloud registering method. The multi-scale normal feature point cloud registering method is characterized by including the steps that two-visual-angle point clouds, including the target point clouds and the source point clouds, collected by a point cloud obtaining device are read in; the curvature of radius neighborhoods of three scales of points is calculated, and key points are extracted from the target point clouds and the source point clouds according to a target function; the normal vector angular deviation and the curvature of the key points in the radius neighborhoods of the different scales are calculated and serve as feature components, feature descriptors of the key points are formed, and a target point cloud key point feature vector set and a source point cloud key point feature vector set are accordingly obtained; according to the similarity level of the feature descriptors of the key points, the corresponding relations between the target point cloud key points and the source point cloud key points are preliminarily determined; the wrong corresponding relations are eliminated, and the accurate corresponding relations are obtained; the obtained accurate corresponding relations are simplified with the clustering method, and the evenly-distributed corresponding relations are obtained; singular value decomposition is carried out on the final corresponding relations to obtain a rigid body transformation matrix.
Owner:HARBIN ENG UNIV

Image super-resolution reconstruction method based on multitask KSVD (K singular value decomposition) dictionary learning

The invention discloses an image super-resolution reconstruction method based on multitask KSVD (K singular value decomposition) dictionary learning, mainly aims at solving the problem that the quality of a reconstructed image of the existing method is relatively reduced seriously under a high-magnification factor. The method comprises the following steps of: inputting a training image, filteringthe image to extract characteristics; extracting tectonic characteristics vector sets of small characteristic blocks, and clustering to obtain sample pair sets {(H1, L1), (H2, L2), ..., (HK, LK)} of K to high resolution and low resolution; developing K high-resolution dictionaries Dh1, Dh2, ..., DhK and corresponding low-resolution dictionaries Dl1, Dl2, ..., DlK from the K groups of sample pair sets by means of a KSVD method; encoding low-resolution patterns input in the low-resolution dictionaries Dl1, Dl2, ..., DlK; obtaining an initial reconstruction image by encoding and high-resolution dictionaries Dh1, Dh2, ..., Dh; then implementing local constrained optimization of the initial reconstruction image; and compensating residual errors and implementing global optimization treatment toobtain a final reconstruction image. The image super-resolution reconstruction method based on multitask KSVD dictionary learning has the advantages that the various natural images can be reconstructed, the quality of the reconstructed image can be effectively improved under the condition of a high-magnification factor, and the method can be applied to the recover and identification of human, animal, plant and building and other target objects.
Owner:XIDIAN UNIV

Image classification method based on hierarchical SIFT (scale-invariant feature transform) features and sparse coding

InactiveCN103020647AReduce the dimensionality of SIFT featuresHigh simulationCharacter and pattern recognitionSingular value decompositionData set
The invention discloses an image classification method based on hierarchical SIFT (scale-invariant feature transform) features and sparse coding. The method includes the implementation steps: (1) extracting 512-dimension scale unchanged SIFT features from each image in a data set according to 8-pixel step length and 32X32 pixel blocks; (2) applying a space maximization pool method to the SIFT features of each image block so that a 168-dimension vector y is obtained; (3) selecting several blocks from all 32X32 image blocks in the data set randomly and training a dictionary D by the aid of a K-singular value decomposition method; (4) as for the vectors y of all blocks in each image, performing sparse representation for the dictionary D; (5) applying the method in the step (2) for all sparse representations of each image so that feature representations of the whole image are obtained; and (6) inputting the feature representations of the images into a linear SVM (support vector machine) classifier so that classification results of the images are obtained. The image classification method has the advantages of capabilities of capturing local image structured information and removing image low-level feature redundancy and can be used for target identification.
Owner:XIDIAN UNIV

Method for extracting and fusing time, frequency and space domain multi-parameter electroencephalogram characters

The invention relates to a method for extracting and fusing time, frequency and space domain multi-parameter electroencephalogram characters, which comprises the following steps: 1) collecting an electroencephalogram signal; 2) performing data pre-processing on the electroencephalogram signal; 3) extracting Kc complexity, approximate entropy and wavelet entropy from the pre-processed data; 4) on the basis of AMUSE algorithm, acquiring an electroencephalogram singular value decomposition matrix parameter; 5) performing character selection on the time, frequency and space domain character parameters for the extracted Kc complexity, approximate entropy, wavelet entropy and electroencephalogram singular value decomposition matrix parameters; 6) utilizing a SVM classifier to fuse and classify the four parameters of the time, frequency and space domains after the character selection. According to the method provided by the invention, the Kc complexity, the approximate entropy, the wavelet entropy and the electroencephalogram singular value decomposition matrix parameter can be selected for comprehensively presenting electroencephalogram character information, and then subsequent effective fusion is performed, so that effective support and help can be supplied to early diagnosis assessment for the brain functional disordered diseases, such as, Alzheimer disease, mild cognitive impairment, and the like.
Owner:秦皇岛市惠斯安普医学系统股份有限公司 +1

SAR (Synthetic Aperture Radar) image segmentation method based on dictionary learning and sparse representation

The invention discloses a SAR (Synthetic Aperture Radar) image segmentation technique based on dictionary learning and sparse representation, and mainly solves the problems that the existing feature extraction needs a lot of time and some defects exist in the distance measurement. The method comprises the following steps: (1) inputting an image to be segmented, and determining a segmentation class number k; (2) extracting a p*p window for each pixel point of the image to be segmented so as to obtain a test sample set, and randomly selecting a small amount of samples from the test sample set to obtain a training sample set; (3) extracting wavelet features of the training sample set; (4) dividing the training sample set by using a spectral clustering algorithm; (5) training a dictionary by using a K-SVD (Kernel Singular Value Decomposition) algorithm for each class of training samples; (6) solving sparse representation vectors of the test sample on the dictionary; (7) calculating a reconstructed error function of the test sample; and (8) calculating a test sample label according to the reconstructed error function to obtain the image segmentation result. The invention has the advantages of high segmentation speed and favorable effect; and the technique can be further used for automatic target identification of SAR images.
Owner:XIDIAN UNIV

Three-dimensional position reconstructing method based on ISAR (inverse synthetic aperture radar) image sequence for scattering point

InactiveCN102353945ASolve the unknownSolve the problem that the viewing angle parameters are difficult to obtainRadio wave reradiation/reflectionSingular value decompositionInterferometric synthetic aperture radar
The invention discloses a three-dimensional position reconstructing method based on an ISAR (inverse synthetic aperture radar) image sequence for a scattering point, which comprises the following four links: a target ISAR image gives the distribution information of a target strong scattering point in a radial direction and a transverse direction based on a distance-Doppler high resolution basic principle; data correlation gives a corresponding relationship between two-dimensional projection points in the ISAR image sequence and is realized by utilizing a flight path initialization method through extracting the one-dimensional radial distance information of all the scattering points in an image sequence; an observing matrix is obtained through the following steps: obtaining a target ISAR image sequence through a period of time of sampling, and after the data correlation, and combining the two-dimensional position coordinates of all the corresponding projection points in a sequence to form the observing matrix, so as to form three-dimensional reconstructed known information; and a position matrix is solved through the following step: solving the optimal estimation of a three-dimensional position matrix of a target scattering point from a subspace through carrying out singular value decomposition on the observing matrix and by utilizing a rank theory and using the orthogonality of a projection space as a constraint condition, so as to obtain a target three-dimensional reconstructed image.
Owner:BEIHANG UNIV

Image super-resolution reconstruction method based on self-similarity and structural information constraint

The invention discloses an image super-resolution reconstruction method based on self-similarity and structural information constraint. The image super-resolution reconstruction method based on the self-similarity and the structural information constraint comprises the achieving steps: (1) taking z images from an image base, carrying out imitating quality degradation on each image, generating a low-resolution image, and constructing a dictionary training sample set; (2) in the dictionary training sample set, learning a pair of high resolution ratio dictionary and low resolution ratio dictionary through a kernel singular value decomposition (K-SVD) method; (3) for a to-be-processed low-resolution image Xt, with scale rotation transform utilized, searching k similar blocks {p1,p2,...,pk} which are mostly similar with an image block xi; (4) carrying out constraint solution on the image block xi through the obtained k similar blocks to obtain a sparse presentation coefficient A; (5) obtaining k reconstruction results through the sparse presentation coefficient A combined with a high-resolution dictionary DH; (6) utilizing a low rank presentation model, amending a similarity degree of the reconstruction results with the similar blocks {p1,p2,...,pk} under the low resolution utilized; (7) obtaining a final result through the amended similarity degree combined with the reconstruction results; and repeating the steps in sequence and obtaining a final high-resolution image YH. The image super-resolution reconstruction method based on the self-similarity and the structural information constraint has the advantages that structural information of the reconstruction results keeps good, and the image super-resolution reconstruction method can be used for image recognition and target classification.
Owner:XIDIAN UNIV
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