429 results about "Data dimensionality reduction" patented technology

The invention discloses an effective micro-expression automatic identification method which comprises the steps of micro-expression frame sequence preprocessing, micro-expression information data study and micro-expression identification. The method for micro-expression frame sequence preprocessing comprises the steps that frames of obtained micro-expression sequences are detected, data of an image of each frame are extracted so that graying processing can be conducted on the data, and all the micro-expression sequences are interpolated into the frame of the unified number through the linear interpolation method. The method for micro-expression information data study comprises the steps that the micro-expression sequences obtained in the preprocessing stage are written in a tensor mode, then, the intra-class distance of the same class of micro-expressions is minimized in a tensor space through the discriminating analysis method of tensor expression and the between-class distance of different classes of micro-expressions is maximized, so that data dimension reduction is achieved, and characteristic data are ranked in a vectorized mode according to a class discriminating capacity descending order. A nearest neighbor classifier is used for micro-expression identification. Compared with the methods of MPCA, GTDA, DTSA and the like, the effective micro-expression automatic identification method has the advantages of being high in rate of identification, low in computer performance requirement and easy to achieve.

Provided is a hyperspectral data dimensionality-reduction method based on a sparse and low-rank representation graph, sparse representation characteristics and low-rank representation characteristics with structures for maintaining global data are obtained via the L1 norm, and the method maintains the low-rank characteristics of a graph via a nuclear norm. The method includes following technical contents: 1) certain data is selected from original hyperspectral data as a training sample; 2) the sparse and low-rank representation graph of the selected training sample is constructed; and 3) an optimal projection matrix is sought via an optimization criterion so that the characteristics of the graph constructed in step 2 are maintained in a low-dimensional manifold space after projection. The characteristics of sparse and low-rank representation between sampling points are learned in an original space, data is projected to the low-dimensional manifold space via a conversion projection matrix, and the characteristics of sparse and low-rank representation between the sampling points can also be maintained.

The invention relates to a streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation. The method comprises the following steps that: S1: presetting data segments and compression parameters; S2: carrying out data scanning on streaming time series data in a slide window way, and entering a streaming data buffer zone; S3: judging whether the fitting error of an initial segmented data segment exceeds an ME_ES (Maximum Error for Entire Segment) or not, carrying out reservation if the fitting error of the initial segmented data segment exceeds the ME_ES, and marking the initial data segment as "inseparable", and if the fitting error of the initial segmented data segment does not exceed the ME_ES, carrying out secondary optimal segmentation; and S4: moving the data segment which is marked as "inseparable" in the streaming data buffer zone out of the streaming data buffer zone, judging whether the streaming time series data to be processed is in the presence or not, if the streaming time series data to be processed is in the presence, returning to the S2, and otherwise, ending. By use of the method, data dimensionality reduction execution efficiency is guaranteed to a high limit, the fitting accuracy of data simplified representation is optimized to a certain range, and accuracy and the execution efficiency of data representation can be improved.

The invention relates to a data mining improved type K mean value clustering method based on linear discriminant analysis, namely an LKM algorithm. Firstly, an LDA is adopted to conduct linear dimensionality reduction on an original n-dimensional data set A to obtain a one-dimensional data set Y, then a k mean value clustering algorithm is adopted to conduct clustering analysis on the data set Y after dimensionality reduction, and final results are output. The method that data dimensionality reduction is combined with the K mean value clustering method is adopted, and defects of the k mean value clustering algorithm on high-dimensional data are overcome through the data dimension reduction technology. The aims of lightening dimensionality curses and eliminating other uncorrelated attributes in high-dimensional space are achieved through data dimension reduction. Meanwhile, the performance of the k mean value clustering algorithm for processing the high-dimensional data is also improved, and the correlated defects of the k mean value clustering algorithm are overcome.

The invention provides a method for reducing dimensions of high spectroscopic data based on a factorial analysis model, comprising the following steps: (1) reading in the high spectroscopic data; (2) establishing the factorial analysis model for dimension reduction of the high spectroscopic data; (3) calculating average of data, covariance matrix and correlation matrix; (4) calculating proper value and standard eigen vector of the correlation matrix of the data; (5) carrying out solving on factor loading matrix by a parametric estimation method; (6) calculating covariance of special factors and communality of data variables in the factorial analysis model; (7) calculating the biggest factor loading spin matrix based on variance; (8) calculating factor scores by a least square method based on variance; and (9) obtaining eigen dimensionality characterizing high spectroscopic data, thereby realizing dimension reduction of the high spectroscopic data. The method is an automatic method for dimension reduction of the high spectroscopic data, which can effectively remove relativity of wave bands of the high spectroscopic data and improve separability of different categories of ground objects.

The invention discloses a data dimension reduction method based on neighborhood rule. The method includes the following steps: firstly, a spherical neighborhood of present sample points is established by using the geometric spherical-modelling theory and all the sample points contained in the spherical neighborhood are adopted as candidate neighbor points, thus not only preserving the effectivity of the dimension reduction capability when data sets are sparse but also getting the advantages of low-sensitivity to isolated points and good stability of the preserved topological structure; then a data relevance matrix more matching semantics can be obtained by relevance measurement based on route clusters to update the candidate neighbor points in the spherical neighborhood and optimize the regular neighborhood space of the present sample points, thus improving the phenomenon that the dimension reduction of sample sets provided with folded curved faces is apt to suffer the integrated-structure distortion in case of heterogeneous data distribution. The experiments on different sample sets demonstrate that the method provided by the invention is available and effective.

The present invention provides a liquid crystal hyperspectral calculation imaging measurement device of three-dimensional encoding. The device comprises a front-end lens 2, a wave band selection and splitting module 3, a space encoding module 4, a collimating lens 5, an area-array detector 6, a data storage module 7 and a calculation reconfiguration module 8; and based on the three-dimensional encoding, the measurement device performs projection measurement of the three-dimensional spectral data of an object consisting of two-dimensional space information and one-dimensional spectral information in the random encoding information, and performs dimensionality reduction of the hyperspectral data at the data collection phase to obtain the compressed hyperspectral data with the selected central wavelength. Compared to the traditional hyperspectral imaging system, the liquid crystal hyperspectral calculation imaging measurement device and method of three-dimensional encoding can realize the compressing sampling on the space, and can perform spectrum selection at the data collection phase so as to realize the data dimensionality reduction, avoid data redundancy, reduce the data volume, improve the information utilization and facilitate rear-end transmission and storage.

The invention discloses a multi-index fused hyperspectral remote sensing image dimensionality reduction method. Dimensionality reduction can be efficiently and rapidly achieved through a band selection method. At first, subspace decomposition is carried out on obtained hyperspectral remote sensing images, the entropy and the standard distance between mean values of a band and correlation coefficients between bands are fused by means of a method based on Choquet fuzzy integral in each subspace, and then band selection is carried out according to unified indexes to achieve data dimension reduction.

The invention discloses a residual service life prediction method for large-scale equipment based on multi-parameter feature fusion. The method comprises the steps: obtaining multiple sensor time sequence parameters of large-scale equipment in a laboratory through a large-scale online monitoring system; performing regression analysis on the multi-parameter continuous values by using a ReliefF algorithm, and obtaining a parameter type with relatively large correlation with the equipment state through feature weight screening; performing data dimension reduction and feature extraction on the screened parameters based on a principal component analysis method, and obtaining a health index representing the operation state of the large-scale equipment through weight fusion; constructing an HMM model based on an expectation maximization algorithm, taking the health indexes as a training set for model training, and finding a grading model used for evaluating the equipment health state corresponding to the current health index; calculating an exponential likelihood value through a Viterbi algorithm to obtain a health index nearest to the likelihood value, and predicting an exponential difference by using a weighted average method to obtain a health state fitting curve; and calculating a residual service life prediction value of the large-scale equipment.

The invention relates to the technical field of using electroencephalogram for identification. The invention provides a method capable of more comprehensively reflecting and analyzing information of electroencephalogram, extracting effective electroencephalogram feature parameters with obvious individual variation from the information and realizing the aim of identification. Therefore, the technical scheme of the invention is as follows: the resting electroencephalogram identification method based on bilinear model comprises the following steps: using an electrode cap worn on the head of a subject to collect the original resting electroencephalogram signals; processing the original resting electroencephalogram signals; establishing a composite model with of linear and nonlinear components; adopting main components to analyze PCA and perform data dimension reduction; and performing identification based on a support vector machine. The invention is mainly used for identification.

The invention discloses a method for carrying out producing area tracing on radix salviae miltiorrhizae by combining determination of the content of microelements and rare-earth elements with multivariable statistical analysis. According to the method disclosed by the invention, difference analysis is carried out on all elements of all samples by adopting one-way analysis of variance. When p is less than 0.05, element data of different producing areas has obvious difference. Main component analysis is used for data dimension reduction analysis and linear judgment analysis is used for estimating whether radix salviae miltiorrhizae medicinal materials from the different producing areas have remarkable difference or not. The invention provides the method for carrying out producing area tracing on the radix salviae miltiorrhizae; and the method has the advantages of sensitivity, rapidness and high judgment accuracy.

The invention discloses a video stream image-based block signal conversion and target detection method and a block signal conversion and target detection device. according to the method, a video imageis subjected to grid segmentation, and the signal fluctuation value of each region at a video monitoring moment is represented by means of the variance values of all pixel values in the above segmented block region of segmented two-dimensional sub-images. In this way, the data dimensionality reduction treatment of a two-dimensional image to a one-dimensional signal is completed. An integral videoimage is converted into a plurality of one-dimensional signal waveforms to be detected. For segmented sub-images in each region of the image, corresponding one-dimensional signal data is obtained. Asa result, through the display method of real-time waveforms, the noise reduction treatment is carried out by means of the smoothing and filtering method during the digital signal processing process.The weak change of the background in sub-regions is excluded. When a foreground object or an abnormal condition occurs, the one-dimensional signal shows a strong fluctuation change. As a result, the abnormal fluctuation existing in the video stream can be obviously judged. In this way, whether the foreground target appears in the video detection area or not can be judged.

The invention discloses a deep learning method for flow abnormity detection of an intelligent power grid server. According to the method, on the basis of the high-dimensional time sequence data information for monitoring the flow of a power grid server, an Encoder-Dector deep neural network based on two layers of LSTM neurons is utilized to construct the flow normal state estimation, and based onthe estimated normal flow behavior, the distribution condition of the residual error between the normal flow behavior and the measured value is analyzed, and finally a confidence interval is given asa control limit for judging the abnormal behavior, thereby realizing the detection of the abnormal flow behavior of the smart grid server. According to the method, the nonlinear relation between variables can be dynamically approximated in a self-adaptive mode, the manual intervention is not needed for selecting variable characteristics, the data dimension reduction is not needed, the certain universality is achieved, and the method has very important scientific significance and application value for preventing severe faults.

The invention discloses a state evaluation method of a communication network of an intelligent substation based on clustering and neural network. The method comprises the following steps of: introducing the weight obtained by an analytic hierarchy process into a standard Euclidean distance space algorithm for data dimensionality reduction; then, dividing a network abnormal state into five classesby using a clustering method; taking a classification result as an ideal output basis of a neural network model training sample; and finally, building a neural network model based on nine evaluation indicators, and carrying out state evaluation on the communication network of the intelligent substation. The state evaluation method uses an evaluation model combining the clustering and the fuzzy neural network; therefore, the state can be effectively evaluated; the interaction between influencing factors can be well characterized; and more real-time and accurate evaluation results can be obtained.

The invention provides an unsupervised clustering method and device for power system operating conditions, wherein the method comprises the following steps: obtaining power system power flow cross sections to be analyzed at different times, and constructing power flow vectors corresponding to each power flow cross section, wherein all power flow vectors constitute power flow vector spaces; using at-distribution random nearest neighbor embedding algorithm for data dimensionality reduction in the power flow vector space; using a hierarchical clustering algorithm for clustering analysis of the reduced dimension power flow vector space to obtain the clustering result of the operation conditions of the power system to be analyzed. In the clustering analysis of power system operating conditions, in addition to considering trend information, power network topology information is also used, the t-distribution stochastic nearest neighbor embedding algorithm is utilized to reduce the dimensionof power flow vector can effectively reduce the computational complexity while preserving the local structure of the original data. The hierarchical clustering algorithm is utilized to cluster the reduced dimension power flow vector can perform better in dealing with the complex distribution of samples.

The invention relates to a time series data classification method based on multi-level shape. The method comprises the following steps: 1) preprocessing time series data: carrying out data dimension reduction processing on an original time series by using an SAX method; 2) obtaining an initial sub-sequence of the time sequence: extracting a sub-sequence set in the time sequence by a window slidingmethod, and indirectly controlling the extraction length of the sub-sequence by changing and adjusting the size of a window; step 3) discovery and extraction of a multi-level Shapelet candidate set:filtering and merging the candidate set through the proposed multi-level Shapelet framework, and selecting the Shapelet with large information gain as the candidate set; and (4) carrying out Shapeletconversion and constructing a classifier. According to the invention, the method is adopted, an efficient multi-level shapelet candidate set filtering model is provided, the number of Shapelet candidate sets is effectively reduced, the Shapelet sets with high classification capacity are rapidly screened, and then effective classification of time series data is achieved through an ELM classifier.

A mineral identification method based on full-spectrum-segment hyperspectral remote sensing data is disclosed. The method comprises the following steps of (1) reading hyperspectral data in different wave band ranges; (2) carrying out minimum noise separation on an image and carrying out data dimensionality reduction; (3) calculating an information entropy of a pixel in a minimum noise separation result obtained in the step (2), setting a threshold value and extracting the pixel with a small information entropy; (4) corresponding the pixel extracted in the step (3) to an original image according to a pixel position, acquiring spectral characteristic parameter, and comparing with and marking the spectral characteristic parameter of a mineral spectral curve in a spectral library; (5) inputting a marked sample into a learning device and training the learning device to obtain a mineral identification result of each single wave band range; and (6) based on a main body majority voting method, fusing each wave band range identification result and completing full-spectrum-segment mineral identification. By using the method of the invention, higher identification accuracy can be acquired when prior information of an identification area is less, and full-spectrum-segment data can be used to make an identification result be comprehensive and accurate.

The present disclosure relates to a motion trajectory representation method and device, and belongs to the technical field of data processing. The method includes: acquiring k position data corresponding to the motion track of the target object; determining the access situation of the motion track to preset n hotspot areas according to the k position data, and the hotspot area is calculated according to several historical motion tracks The obtained geographical area whose visit popularity is higher than the preset threshold; determines the trajectory feature vector corresponding to the movement trajectory according to the access situation, and the trajectory feature vector is used to represent the characteristics of the movement trajectory. This disclosure solves the problem that the large amount of data brought by directly using position data to represent the motion trajectory is not conducive to the calculation and processing of the subsequent analysis of the motion trajectory; In order to simplify the representation, the effect of data dimensionality reduction is achieved, and it is convenient for the subsequent analysis and calculation of the motion trajectory.

The invention discloses a hyperspectral remotely-sensed data dimensionality reduction method based on improved hierarchical clustering. The hyperspectral remotely-sensed data dimensionality reduction method comprises the following steps of: selecting hyperspectral remotely-sensed image data to be analyzed, wherein the hyperspectral remotely-sensed image data comprise L wave bands; calculating a spectral distance between every two wave bands by a security identification (SID) algorithm to acquire a clustering center extracted for setting a spectral distance matrix and a number k of wave bands to be selected; performing clustering analysis on the image data by a hierarchical clustering method based on a similarity distance matrix; acquiring k clustering center data to finish a feature extraction process; and selecting the most representative wave band from each clustering center to acquire k wave bands to finish a feature selection process. By the method, the dimensionality reduction efficiency can be improved; and the data information loss caused by the conventional hyperspectral remotely-sensed data dimensionality reduction method is reduced.

The embodiment of the invention discloses a post-loan risk assessment method and a device, and a storage medium, and the method comprises the following steps: carrying out the preprocessing of storedloan data, including data dimension reduction, data re-classification, data combination and data cleaning, and the loan data after classification comprises static data and dynamic data; performing feature engineering on the preprocessed dynamic data to extract corresponding feature engineering features; constructing an evaluation model based on a deep learning network, and training the evaluationmodel by adopting expert features and feature engineering features in the financial model; and performing data evaluation based on the evaluation model and outputting a final approval list. Accordingto the method, the efficiency of post-loan data analysis can be improved, the precision of a risk assessment model is improved, the predictable times of default are increased, and finally, the post-loan risk can be effectively controlled.

The invention discloses an application method of a machine learning classification model in adolescent autism auxiliary diagnosis. The method is characterized by being implemented according to the following mode. The method comprises the steps of 1, establishing a model training method; 2, constructing a model evaluation index; 3, performing characteristic engineering of the autism auxiliary diagnosis system; 4, performing data dimension reduction processing; 5, carrying out feature selection; and 6, carrying out model training and result analysis. According to the invention, a machine learning method is introduced into the field of autism research; the high efficiency and the reliability brought by the method are greatly helpful to the auxiliary diagnosis of autism. The application fieldof the invention can be embodied in: (1) disease diagnosis and treatment, (2) smoking addiction, network addiction and network game addiction, (3) cognition and other health fields, and the like.