34 results about "Linear discrimination analysis" patented technology

A standard model creating apparatus which provides a high-precision standard model used for pattern recognition such as speech recognition, character recognition, or image recognition using a probability model based on a hidden Markov model, Bayesian theory, or linear discrimination analysis; intention interpretation using a probability model such as a Bayesian net; data-mining performed using a probability model; and so forth. The standard model creating apparatus includes a reference model preparing unit that prepares at least one reference model; a reference model storing unit that stores the reference model prepared by the reference model preparing unit; and a standard model creating unit that creates a standard model by calculating statistics of the standard model so as to maximize or locally maximize the probability or likelihood with respect to the reference model stored in the reference storing unit.

The invention discloses a method and apparatus for training voiceprint recognition system, belonging to the technical field of voiceprint recognition. The method comprises a step of determining the identity vector of each section of speech in a speed training set, and dividing the identity vectors of the speech of a same user in the determined identity vectors into a category, a step of establishing a first function for calculating the first value corresponding to each category, wherein the first value is the sum of the similarities of a first identity vector normalized by using a normalization matrix in a corresponding category and other identity vectors normalized by using the normalization matrix in the corresponding category, a step of determining the normalization matrix which allows the sum of the first value of each category to be maximum, and a step of normalizing the identity vector of the speech obtained in the voiceprint recognition system by using the determined normalization matrix. The problem of the low improvement of the accuracy of the voiceprint recognition by using an identity vector which is subjected to linear discrimination analysis processing in the related technology is solved, and the accuracy of the voiceprint recognition is improved.

Provided is an image feature extraction method based on global and local structure fusion, characterized by comprising: 1) constructing a weight adjacent map; 2) determining laplacian matrix of similar matrix, degree matrix and images, 3) determining scatter matrix inside the kind and between the kind; 4) determining projection matrix, 5) identifying. The invention provides a feature extraction method of fusing the global structure information and the local structure information, wherein complex features fused of the global feature and the local feature are extracted, thereby the method has strong resolving power. The method not only has the characteristics of holding the reflection method locally, namely holding the characteristics of manifold structure of data; moreover has the characteristics of linear discrimination analysis method, namely assembling the date of the kind more compact to enlarge the distance between the kinds. The invention is applied in image recognition, therebyincreasing identifying performance.

The present invention discloses a multi-attribute decision tree power grid stability margin assessment method based on linear discrimination analysis. a key variable discovery model is established based on the offline simulation data and the real-time monitoring data of a power grid to perform effective screening of historical sample data to reduce the data dimensions, a combination relation model among key variables is established to discover the association relation among the variables, extract combination features capable of reflecting important degree contrast of each variable, establish the association relation between the power grid operation state and the transient stability margin, determine the main reasons of system stability level changing, form a concise and accurate knowledge rule base and regulate the decision reference so as to rapidly assess the current stability level according to the system operation state, provide quantification information support for operators' auxiliary decisions and improve the standardization, the rapidity and the adaptive capability of the power grid stability assessment, and therefore the multi-attribute decision tree power grid stability margin assessment method based on the linear discrimination analysis has wide application prospects.

The invention provides a printed font character identification method based on Arabic character set which comprises, extracting region information, character font information, and constituent part information unique for Arabic character set, carrying presorting, determining character class subset of the input character, extracting direction characteristic for reflecting the character stroke composition information, finally employing two steps of characteristic optimization processing including, characteristic dressing, eigen transformation by integrating linear discrimination analysis (LDA) and K-L transformation, finally proceeding categorization judgment through modified quadratic discriminating function (MQDF) statistical classification device.

The invention discloses an LDoS attack detection method based on frequency domain feature fusion, which belongs to the field of computer network security. The method includes: firstly, obtaining the network data message in the router to obtain the sample sequence; then, based on discrete Fourier transform and discrete wavelet transform, transforming the sample sequence from the time domain to the frequency domain, fully extracting the sample sequence Frequency-domain features; then, linear discriminant analysis is used to fuse the extracted frequency-domain features to obtain decision features, which can significantly improve the classification performance of features; finally, input the decision features into the pre-trained single-class classification anomaly detection model, And according to the output of the anomaly detection model, the network data packets in the unit time are judged and detected. If the output of the anomaly detection model is -1, it is determined that an LDoS attack has occurred in the network in the unit time. The detection method based on frequency domain feature fusion proposed by the invention can efficiently, quickly and accurately detect LDoS attacks.

The invention belongs to the field of mode recognition and a brain-machine interface and discloses a motor imagery electroencephalogram voting strategy sorting method based on extreme learning machines. The motor imagery electroencephalogram voting strategy sorting method comprises the following steps: dividing an original motor imagery electroencephalogram into S sections of sub-signals; carrying out dimensionality reduction on each section of sub-signal by a principal component analysis method; carrying out secondary dimensionality reduction on a feature vector subjected to the dimensionality reduction by a linear discrimination analysis method; carrying out the same processing on the S sections of sub-signals to finally obtain S (K-1)-dimensional feature vectors, and combining the S (K-1)-dimensional feature vectors to finally obtain an S*(K-1)-dimensional feature; and transmitting the S*(K-1)-dimensional feature into a plurality of ELM (Extreme Learning Machine) sorting devices so as to obtain a final sorting result by utilizing a voting sorting strategy. The invention provides a voting sorting strategy based on the ELMs; compared with a traditional multi-time ELM average accuracy scheme, the method provided by the invention has the advantages that the sorting accuracy is improved under the condition of not influencing the training sorting low consumption.

The invention discloses a FY-3D infrared hyperspectral cloud detection method based on linear discriminant analysis, and relates to the technical field of satellite remote sensing. The present invention uses the traditional imager-based cloud detection method to match the cloud detection segment products of MERSI with HIRAS pixels to obtain HIRAS data with cloud labels to form a training data set, and then use the linear discriminant analysis algorithm to train the data set , in the training process, the grid search method is used to select the appropriate linear discriminant analysis algorithm hyperconstant, and the model parameters with the best generalization performance are obtained. Finally, the linear discriminant analysis cloud detection model obtained by training is used for the infrared hyperspectral of YF-3D Data cloud detection processing. The linear discriminant analysis cloud detection model of the invention can achieve a classification accuracy of 0.95, and the prediction speed is about 58 times that of the traditional method, showing good generalization performance.

The present invention provides an En-ULLELDA-based (ensemble-unified locally linear embedding and linear discriminant analysis) method of multi-view model recognition. The method comprises the following steps: firstly, selecting a vehicle ROI (region of interest); secondly, extracting the high-level information that can reflect the model as features through Gabor wavelet transform; then, carrying out the dimension reduction of features on the basis of a novel manifold learning algorithm En-ULLELDA, so as to transform the features to a low-dimensional space; and obtaining the corresponding models from the features of a test sample by a classifier during the recognition. According to test results, the invention can effectively handle the problem of multi-view model recognition on urban roads; and the invention can eliminate the effects of parameter variation on the recognition results by employing the integration technology, so that the invention has higher robustness.

The present invention relates to a face verification method and system. The method includes: using principal component analysis and linear discriminant analysis to respectively preprocess high-dimensional face feature data, including setting the data dimension after principal component analysis dimension reduction; establishing a discriminant High-order Boltzmann machine, set the number of nodes in the hidden layer; use tensor diagonalization strategy to reduce the model parameters of the discriminative high-order Boltzmann machine; input the paired face data into In the discriminative high-order Boltzmann machine, the stochastic gradient descent algorithm is used to maximize the conditional probability of the relationship category, thereby iteratively optimizing the weight of the Boltzmann machine, so as to obtain the final discriminative high-order Boltzmann machine; input the paired face data to be verified into the discriminant high-order Boltzmann machine model, and obtain the corresponding verification result data. The invention enhances the discriminative power of the model by introducing the data relationship category information into the unsupervised Boltzmann machine model, and is more suitable for face verification with precision requirements.