2152 results about "Singular value decomposition" patented technology

A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space. The similarities of those documents can be ascertained from the position of their respective pseudo-document vectors in the representative semantic space.

A computer-based information search and retrieval system and method for retrieving textual digital objects that makes full use of the projections of the documents onto both the reduced document space characterized by the singular value decomposition-based latent semantic structure and its orthogonal space. The resulting system and method has increased robustness, improving the instability of the traditional keyword search engine due to synonymy and / or polysemy of a natural language, and therefore is particularly suitable for web document searching over a distributed computer network such as the Internet.

A computer-implemented system and method for processing text-based documents. A frequency of terms data set is generated for the terms appearing in the documents. Singular value decomposition is performed upon the frequency of terms data set in order to form projections of the terms and documents into a reduced dimensional subspace. The projections are normalized, and the normalized projections are used to analyze the documents.

An audio fingerprinting system and method. A server receives an audio fingerprint of a first audio piece, searches a database for the audio fingerprint, retrieves an audio profile vector associated with the audio fingerprint, updates user preference information based on the audio profile vector, and selects a second audio piece based on the user preference information. The audio fingerprint is generated by creating a matrix based on the frequency measurements of the audio piece, and performing a singular value decomposition of the matrix. To expedite the search of the database and to increase matching accuracy, a subset of candidates in the database is identified based on the most prominent musical notes of the audio piece, and the search is limited to the identified subset. One of the attributes of the audio profile vector is a particular audio class. An identifier for the audio class is generated based on an average of audio fingerprints of the audio pieces belonging to the audio class.

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.

A method and system for blind estimation of time and frequency parameters of an incoming signal. A spectrogram of the signal is processed using singular value decomposition (SVD) and ICA (independent component analysis) techniques. Time and frequency parameters are estimated from time and frequency eigenvectors, respectively.

A system and method for transmitting a plurality of input data symbol sub-streams over a plurality of spatial-subspace channels of a sub-carrier between a transmitter and the receiver. The plurality of input data symbol sub-streams are partitioned in a plurality of super-frames of data and weighted by a weight matrix derived from the singular value decomposition of a channel matrix corresponding to the sub-carrier by applying a partial SVD algorithm. The transmitter further inserts subspace training symbols into the plurality of input data symbol sub-streams and the receiver periodically processes the sub-space training symbols during each super-frame of the plurality of super-frames for estimating output data related to the input data symbol stream.

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.

In a technique for video segmentation, classification and summarization based on the singular value decomposition, frames of the input video sequence are represented by vectors composed of concatenated histograms descriptive of the spatial distributions of colors within the video frames. The singular value decomposition maps these vectors into a refined feature space. In the refined feature space produced by the singular value decomposition, the invention uses a metric to measure the amount of information contained in each video shot of the input video sequence. The most static video shot is defined as an information unit, and the content value computed from this shot is used as a threshold to cluster the remaining frames. The clustered frames are displayed using a set of static keyframes or a summary video sequence. The video segmentation technique relies on the distance between the frames in the refined feature space to calculate the similarity between frames in the input video sequence. The input video sequence is segmented based on the values of the calculated similarities. Finally, average video attribute values in each segment are used in classifying the segments.

A method and system is described for more optimally managing the usage of a wideband space-time multipath channel. The wideband space-time multipath channel is decomposed into a plurality of orthogonal sub-channels, where the orthogonal sub-channels having the best signaling characteristics are used for transmitting one or more signal streams. For purposes of decomposing the wideband space-time multipath channel into a plurality of orthogonal sub-channels, channel estimates are determined for each signal propagation path. A closed-form singular value decomposition of the channel corresponding to each receive antenna before coherent combining is utilized to obtain an orthogonal decomposition of the overall effective space-time channel after coherent combining. By using the overall effective space-time channel after coherent combining rather than before coherent combining, the complexity and correspondingly the resources required for obtaining the orthogonal sub-channels is significantly reduced. The method and system further provide for transmit power to be allocated between the selected sub-channels in order to minimize the effective bit-error rate for a fixed average throughput or to maximize average throughput for a fixed minimum effective bit-error rate.

A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space. The similarities of those documents can be ascertained from the position of their respective pseudo-document vectors in the representative semantic space.

Text summarizers using relevance measurement technologies and latent semantic analysis techniques provide accurate and useful summarization of the contents of text documents. Generic text summaries may be produced by ranking and extracting sentences from original documents; broad coverage of document content and decreased redundancy may simultaneously be achieved by constructing summaries from sentences that are highly ranked and different from each other. In one embodiment, conventional Information Retrieval (IR) technologies may be applied in a unique way to perform the summarization; relevance measurement, sentence selection, and term elimination may be repeated in successive iterations. In another embodiment, a singular value decomposition technique may be applied to a terms-by-sentences matrix such that all the sentences from the document may be projected into the singular vector space; a text summarizer may then select sentences having the largest index values with the most important singular vectors as part of the text summary.

The present invention enables execution of the processing using metadata such as content recommendation in consideration to cooccurrence relation among metadata. A matrix generating section generates a metadata matrix having N rows corresponding to N metadata (N: integral number of 1 or more) respectively and M columns corresponding to M metadata (M: integral number of 1 or more). A LSA computing section generated an approximated matrix of a metadata matrix by subjecting the metadata matrix to singular value decomposition. The metadata extracting section computes, for each of the N metadata, an index value such as a feature difference indicating importance of metadata corresponding to the metadata above, and extracts important metadata or unnecessary metadata from among the N metadata. The present invention may be applied to an information processing apparatus for content recommendation.

A channel response matrix is obtained by performing a training process between a transmitter 401 and a receiver 402 to obtain optimal signal phases of the antenna array. Next, a singular-value decomposition (SVD) process is performed to decompose the channel response matrix into a correlation matrix and eigenvalues. Next, a diagonal matrix having square roots of the eigenvalues as its components is obtained. Next, all but one of diagonal components included in the diagonal matrix are replaced with zeros, and optimal setting of the amplitudes and phases of signals to be applied to the antenna array (antenna weight vector) for use in wireless communication between the transmitter and the receiver is obtained based on a channel response matrix that is reconstructed by using the component-replaced diagonal matrix. In this way, when wireless communication is implemented by performing beam forming, the time necessary to find and set a beam direction can be reduced.

Methods and systems of analyzing an operating environment of wireless communication equipment in a wireless communication system are provided. A stimulus in the operating environment at a location of the wireless communication equipment is sensed and linearly expanded in Slepian sequences using a multitaper spectral estimation procedure. A singular value decomposition is performed on the linearly expanded stimulus, and a singular value of the linearly expanded stimulus provides an estimate of interference at the location of the wireless communication equipment.

The present invention provides a rolling bearing fault diagnosis method in various working conditions based on feature transfer learning, and relates to the field of fault diagnosis. The objective ofthe invention is to solve the problem that a rolling bearing, especially to various working conditions, is low in accuracy of diagnosis. The method comprise the steps of: employing a VMD (VariationalMode Decomposition) to perform decomposition of vibration signals of a rolling bearing in each state to obtain a series of intrinsic mode functions, performing singular value decomposition of a matrixformed by the intrinsic mode functions to solve a singular value or a singular value entropy, combining time domain features and frequency domain features of the vibration signals to construct a multi-feature set; introducing a semisupervised transfer component analysis method to perform multinuclear construction of a kernel function thereof, sample features of different working conditions are commonly mapped to a shared reproducing kernel Hilbert space so as to improve the data intra-class compactness and the inter-class differentiation; and employing the maximum mean discrepancy embedding to select more efficient data as a source domain, inputting source domain feature samples into a SVM (Support Vector Machine) for training, and testing target domain feature samples after mapping. Therolling bearing fault diagnosis method in various working conditions has higher accuracy in the rolling bearing multi-state classification in various working conditions.

The invention realizes an SVD-MIMO transmission having resistance to the variations in the channel characteristic, which saves the feedback from the receiver to the transmitter. The receiver updates the current one into a new channel matrix Hnew every 100 OFDM symbols, and performs the reception processing by updating the current one into a new decoding weight matrix Unew acquired by the singular value decomposition of the channel matrix Hnew. On the other hand, the transmitter continues to use the original transmission weight matrix V. The diagonal matrix D is turned into a non-diagonal matrix because of the variations in the channel characteristic, where the elements except for the diagonal elements take the values except for zero. This shows that cross talks are generated at this moment. The receiver acquires the cross talk gains, and cancels the cross talk signals of the reception signal to thereby realize the signal transmission without cross talks in consequence.

Disclosed is a method for transmitting / receiving data by use of feedback information in a closed loop multi input multi output (MIMO) mobile communication system. The method includes the steps of feeding back transmission eigenvector selection information determined through a singular value decomposition (SVD) of a channel matrix and transmitting the transmission eigenvector selection information to a transmitter; and receiving the feedback transmission eigenvector selection information, selecting transmission data according to the received transmission eigenvector selection information, mapping the selected transmission data to at least one transmission antenna, and transmitting the transmission data to a receiver.

A method of image processing using kernel regression is provided. An image gradient is estimated from original data that is analyzed for local structures by computing a scaling parameter, a rotation parameter and an elongation parameter using singular value decomposition on local gradients of the estimated gradients locally to provide steering matrices. A steering kernel regression having steering matrices is applied to the original data to provide a reconstructed image and new image gradients. The new gradients are analyzed using singular value decomposition to provide new steering matrices. The steering kernel regression with the new steering matrices is applied to the noisy data to provide a new reconstructed image and further new gradients. The last two steps are repeated up to ten iterations to denoise the original noisy data and improve the local image structure.

A multiple-input multiple-output (MIMO) communication system is provided for communication between a transmission apparatus having a plurality of transmission antennas and a reception apparatus having a plurality of reception antennas. The reception apparatus performs singular value decomposition (SVD) on channel information for a channel established to the transmission apparatus, and feeds back the SVD-decomposed channel information to the transmission apparatus. The transmission apparatus receives the SVD-decomposed channel information from the reception apparatus, performs QR decomposition on the received SVD-decomposed channel information, and sets multiple transmission antennas to be allocated to the channel, thereby performing beamforming.

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.

Compensation for rendering device non-uniformities is provided for halftoned images. A spatially dependent tone reproduction curve (TRC) provides compensation values. Pixel location information is used to access TRC values. For example, the values are modification values. The modification values are added to the pixel values to generate combined values. Quantization is applied to the combined values to prepare compensated image data for rendering. For example, Rank Ordered Error Diffusion is applied to the combined values. The combined values may include diffused error from previously processed pixels. Gray values may be estimated for the respective pixels. The estimated gray values may be used to access compensation information from a TRC that is both spatially and gray value dependent. Mathematical basis decomposition is used to reduce TRC memory requirements. For example, Discrete Cosine Transformation, Singular Value Decomposition or Principal Component Analysis is used to determine a compact form for the TRC.

Specifically, a method and system that performs MIMO and beamforming at a base station based on an uplink channel sounding (ULCS) from only one of the mobile station antennas and closed-loop multiple input, multiple output (MIMO) schemes based on the singular value decomposition (SVD) of the channel matrix. The ULCS is limited to sounding and the channel uses fewer than an optimal number of transmit antennas (e.g. one for WiMAX). The base station arrays may be configured for a full array transmitting mode or a sub-array transmitting mode.

The invention discloses a diagnosis method for the fault position and the performance degradation degree of a rolling bearing, belonging to the technical field of fault diagnosis for bearings, and solving the problems of low accuracy of diagnosis for fault position and performance degradation degree, and high time consumption of training existing in an intelligent diagnosis method for a rolling bearing in the prior art. A white noise criterion is added in the disclosed integrated empirical mode decomposition method, so that artificial determination for decomposition parameters can be avoided, and the decomposition efficiency can be increased; and via the disclosed nuclear parameter optimization method based on a hypersphere centre distance, the small and effective search region of nuclear parameters in a multi-classification condition can be determined, so that training time is reduced, and the final state hypersphere model of a classifier is given. The intelligent diagnosis method based on parameter-optimized integrated empirical mode decomposition and singular value decomposition, and combined with a nuclear parameter-optimized hypersphere multi-class support vector machine based on the hypersphere centre distance is higher in identification rate compared with the existing diagnosis method. The diagnosis method disclosed by the invention is mainly applied to intelligent diagnosis on the fault position and the performance degradation degree of the rolling bearing.

The disclosed invention implements SVD-MIMO communication efficiently with a less number of high-load calculation required for singular value decomposition (SVD) processing for a channel matrix. A receiver derives a channel matrix H from a reference signal from a transmitter and acquires downlink transmit weights V and receive weights UH by SVD of the channel matrix H. The receiver transmits a reference signal weighted with U* to the transmitter, where U* is a conjugate matrix for U as uplink transmit weights. The transmitter receives the reference signal weighted with U* and separates the signal into downlink transmit weights V and a diagonal matrix D, based on unitary matrix properties.