179 results about "Matrix factorisation" patented technology

In a recommender method, Bayesian Matrix Factorization (BMF) is performed on a matrix having user and item dimensions and matrix elements containing user ratings for items made by users in order to train a probabilistic collaborative filtering model. A recommendation is generated for a user using the probabilistic collaborative filtering model. The recommendation may comprise a predicted item rating, or an identification of one or more recommended items. The recommender method is suitably performed by an electronic data processing device. The BMF may employ non-Gaussian priors, such as Student-t priors. The BMF may additionally or alternatively employ a heteroscedastic noise model comprising priors that include (1) a row dependent variance component that depends upon the matrix row and (2) a column dependent variance component that depends upon the matrix column.

The invention belongs to the technical field of e-commerce information processing, and discloses a cross-domain recommendation data processing method with multiple auxiliary domains and a cross-domainrecommendation data processing system. The method comprises obtaining a scoring matrix of an auxiliary domain, calculating the scoring reliability of a user, carrying out equal-proportion segmented mapping on a threshold value, and emptying scores with the number of scores lower than the threshold value in the auxiliary domain; obtaining clustering level scoring matrix of all domains by using a K-means clustering algorithm, and carrying out matrix decomposition; meanwhile, decomposing the target domain scoring matrix to learn a feature mapping function for the cold start user; evaluating predicted scoring matrix by using an average absolute error. Compared with the prior art, the method has the advantages that K-means clustering algorithm is used in the data processing process for obtaining clustering-level user project scoring matrix combined with all domains, the data sparsity of the cold start user is reduced. The problem that the recommendation effect is not ideal due to poor prediction accuracy of a traditional single auxiliary domain matrix factorization model is solved, the recommendation effect of the recommendation system is improved, and the method has higher universality.

Described is a system for sensor data fusion and reconstruction. The system extracts slices from a tensor having multiple tensor modes. Each tensor mode represents a different sensor data stream of incomplete sensor signals. The tensor slices are processed into demixed outputs. The demixed outputs are converted back into tensor slices, and the tensor slices are decomposed into mode factors using matrix decomposition. Mode factors are determined for all of the tensor modes, and the mode factors are assigned to tensor factors by matching mode factors common to two or more demixings. Tensor weight factors are determined and used for fusing the sensor data streams for sensor data reconstruction. Based on the sensor data reconstruction, hidden sensor data is extracted.

The invention discloses a deep learning method fusing scoring data and comment texts. The influence of scoring data sparsity is relieved by introducing auxiliary information. The preference information and the item characteristics of the user can be obtained by utilizing the comment text, and the score data also contains potential association between the user and the item. Most of existing fusionmodels adopt a matrix decomposition method to process score data, in order to better utilize effective information in the score data, a convolutional neural network is utilized to process comment texts, an attention mechanism is introduced to extract representative comments in comment information, and therefore, the score data fusion accuracy is improved. Therefore, user preferences and project characteristics can be better represented. By processing the score data by using a deep neural network to extract deep features in the score data and fusing the features, the method can predict the score of the project by the user.

The invention relates to a satellite magnetic field data seismic abnormality detection method based on non-negative matrix decomposition. The method comprises a step of removing invalid data accordingto a flag bit and subtracting CHAOS-6 magnetic field model data, and solving a first-order difference of a result to obtain differential data, a step of performing short-time Fourier transform on thedifferential data to construct a non-negative time-frequency amplitude matrix corresponding to magnetic field data, a step of decomposing the non-negative time-frequency amplitude matrix by using a non-negative matrix decomposition method and separating a local influence component generated by an earthquake from a global influence component generated by a sun activity and a geomagnetic activity,a step of selecting the local influence component generated by the earthquake according to an energy ratio and carrying out abnormal track judgment on the component through an over-limit rate method,and a step of accumulating the number of abnormal orbits every day and detecting earthquake abnormality according to the degree of deviation from a background fitting straight line. According to the invention, all data obtained by measurement can be reserved and utilized to research the earthquake, and at the same time, components more related to the earthquake activity can be obtained to effectively perform earthquake abnormality detection.

The invention discloses a social collaborative filtering recommendation method based on federal learning. The social collaborative filtering recommendation method comprises the following steps that 1, a central server selects user clients participating in local training; step 2, E rounds of local training are carried out on the user client, and in each round of local training process, a model loss function is calculated through a matrix decomposition module, a user comparison module and an article comparison module; 3, after the E rounds of local training are completed, a gradient which finally needs to be uploaded is obtained through a gradient protection module, and the gradient is uploaded to a central server for gradient aggregation; step 4, repeating the step 1 to the step 3 to obtain a fully trained user embedding vector and an article embedding vector; and 5, taking the user embedded vector and the article embedded vector as input of a score prediction module to obtain an article sequence recommended to the user. According to the method, the influence of the data non-independent identical distribution problem in federated social recommendation on the model recommendation performance can be effectively relieved.

The invention provides a cloud service QoS prediction method based on multi-source feature learning. The method comprises the following steps of extracting explicit features of users and services by utilizing context data of the users and the services, based on an implicit factor embedding method combining matrix decomposition and a neural network, extracting deep implicit features of users and services from the user-service matrix, splicing the explicit features and the implicit features to obtain a multi-source feature matrix corresponding to the user-service call record, and learning to obtain a local-global feature combination of the multi-source feature matrix by using a joint deep network based on a convolutional neural network, thereby obtaining QoS prediction of the cloud service by the user. According to the method, the high-order feature combination is learned from the multi-source information, and the influence of the feature sequence on the feature combination learning is fully considered, so that the QoS prediction precision is effectively improved.

The invention discloses a sample clustering and feature recognition method based on integrated non-negative matrix factorization. The method comprises: 1, X = {X1, X2... XP} representing multi-view data composed of P different omics data matrixes of the same cancer; 2, constructing a diagonal matrix Q; 3, introducing graph regularization and sparse constraints into the integrated non-negative matrix factorization framework to obtain target functions O1 and O2; 4, solving the target function O1 to obtain a fusion feature matrix W and a coefficient matrix HI; solving the target function O2 to obtain a feature matrix WI and a fusion sample matrix H; 5, constructing an evaluation vector according to the fusion feature matrix W, and identifying common difference features according to the vector; 6, performing functional explanation on the identified common difference characteristics by using GeneCards; and 7, performing sample clustering analysis according to the fusion sample matrix. According to the method, the complementary and difference information of the multiple omics data can be fully utilized to identify the common difference characteristics, clustering analysis can be carriedout on the sample data provided by the multiple omics data, and a calculation method basis is provided for integrated research of different types of omics data.

The current methods and apparatus provide a system that learns a private attribute, such as gender, based on at least one iteration of presenting an item to a user and receiving ratings from the user for this item. In an exemplary embodiment, the system may solicit ratings for strategically selected items, such as movies for example, and then infers the user's gender. Based on the assessed confidence in the demographic selected, the system may repeat the selection, presentation and ratings of another item. The proposed system can strategically select the sequence of items that are presented to the user for a rating. By selecting the next item to be rated based on a maximum posterior probability confidence, a demographic with a certain threshold of confidence can be inferred. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting. Such a system is shown to be feasible and can be carried out using significantly fewer rated items than previously proposed static inference methods.

The present development details a method and apparatus for performing channel emulation of doubly selective scenarios, where the simulation and emulation duration is arbitrarily long for a stationary or non-stationary channel, with non-separable dispersion which is achieved by combining the techniques of channel orthogonalization, decomposition of the correlation tensor in the Doppler domain into frequency-dependent correlation matrices, followed by a matrix factorization of each of the mentioned matrices and, finally, the use of the windowing method to generate arbitrarily long achievements which thereby allows the concatenation of channel realizations coming from the same or different NSSF, thus achieving reproduction of stationary or non-stationary channels, respectively.

A method, apparatus and computer-readable storage medium for determining one or more recommendations by applying efficient adaptive matrix factorization are disclosed. The method comprises causing, at least in part, an iterative performing of the following steps: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period.

The invention discloses a multi-view data missing complementing method for multi-manifold regularization non-negative matrix factorization, which comprises the following steps of: obtaining manifold and global clustering in which unmarked multi-view data tends to be consistent by utilizing a multi-manifold regularization non-negative matrix factorization algorithm through consistency assumption among multiple views; and constructing a multi-view collaborative discrimination model by adopting a view collaborative improved Gaussian mixture method. Pre-calibration of a cluster to which a sample belongs is realized by calculating the cluster relevancy level of the sample with missing data under a non-missing view angle; and establishing a missing data prediction model under a specific view angle by utilizing the consistency of multiple view angles in a low-dimensional space and adopting a multiple linear regression analysis method, thereby realizing accurate data completion under a multi-attribute missing condition. According to the method, large-scale labeled samples are not needed for training, the pre-defined category relation and related characteristics are avoided, and the understanding and discovering capacity of an existing multi-view mining technology for unlabeled multi-source data is improved.

This invention generally relates to data processing hardware, and more particularly to hardware accelerators and related methods for matrix factorisation especially non- negat ive matrix factorisation (NMF). Embodiments of the invention are particularly useful for driving electroluminescent displays such as OLED displays. A matrix factorisation hardware accelerator for determining a pair of factor matrices (R; C) which when multiplied together approximate a target matrix, the hardware accelerator comprising: an input to receive an input data matrix representing said target matrix; a first factor matrix memory for storing row and column data for a first factor matrix (R), said first factor matrix memory having a plurality of first data buses each associated with a respective block of said first factor matrix memory for accessing first factor matrix column data stored in the block; a second factor matrix memory for storing row and column data for a second factor matrix (C), said second factor matrix memory having a plurality of second data buses each associated with a respective block of said second factor matrix memory for accessing second factor matrix row data stored in the block; a matrix of processor blocks, each processor block having: a first processor block data bus coupled to one of said first data buses, a second processor block data bus coupled to one of said second data buses, and a result data output; a processor memory block for storing a portion of a matrix (Q) representing a difference between a product of said pair of factor matrices and said target matrix; and a data processor.

The invention discloses a data-to-text generation method based on fine-grained topic modeling. The data-to-text generation method comprises the following steps: learning semantic representation of each data record on a coding layer based on a bidirectional long-short-term memory network; learning topic distribution corresponding to each data record and word distribution corresponding to each topicbased on a non-negative matrix factorization method to obtain a topic word table corresponding to each data record; based on the semantic representation of each data record in a decoding layer, carrying out text generation by utilizing a long-term and short-term memory network, an attention mechanism and fine-grained topic representation in combination with a topic word table; and performing model training to obtain an optimal text generation result. According to the method, topic distribution of data and word distribution corresponding to topics are mined by utilizing a non-negative matrix factorization method, so that topic consistency between a generated text and a data table is restrained, and a model is guided to learn a more accurate word use mode; a copying mechanism is introducedin the text generation process, and it is guaranteed that the model can accurately generate numerical description.

The method of the invention definitely combines semi-supervised data representation and classification errors into a conventional non-negative matrix factorization framework for joint minimization learning, thereby applying weight coefficient construction and a label propagation process to the projection non-negative matrix factorization, avoiding the negative effects of noise, damage, or aliens included in original data on similarity measures and label prediction results. In addition, the joint minimization process can maintain the neighborhood information and the spatial structure in the projection non-negative matrix factorization process, and obtain an accurate data representation result. In addition, the weight construction and the inductive learning are integrated into a unified model so as to obtain an adaptive weight coefficient matrix, thereby avoiding the difficulty in selecting the optimal neighbor in a traditional algorithm. The method of the invention is an inductive model, can complete the induction and prediction of the data outside a sample, prevents introduction of additional reconstruction process, and has good scalability performance.

Aiming at the clustering problem of a high-dimensional multivariate time sequence, the invention provides a multi-view clustering method based on constrained non-negative matrix factorization, in order to effectively carry out sample clustering, multivariate time sequence data is firstly projected to a multi-relation network, and then a hierarchical method is adopted to generate a plurality of independent views. The method also includes, secondly, clustering the plurality of views by adopting a constrained non-negative matrix factorization method; and finally, solving in an alternate iterativeoptimization mode, and performing k-means clustering on the representation matrix obtained by calculation of the solving result to obtain a clustering result. The correctness and effectiveness of themethod and the algorithm provided by the invention are verified through experiments, and experimental results show that the clustering method has relatively high accuracy and effectiveness.

The invention discloses a sequence recommendation method based on an information spreading and attention mechanism. Calculating the transmission force corresponding to each article according to the input user behavior sequence; correcting the score data of the user; carrying out feature extraction, calculating the correlation between an article at the current time and an article before the current time node by utilizing matrix decomposition, and inputting a correlation result into a Softmax function to output and predict the probability of a next article; and establishing a binary cross entropy loss function, when the loss function does not reach a given condition, inputting the information into the neural network model again for training until an output result meets the given condition, and using the trained model as a final model for sequence recommendation. According to the method, the spreading force information of the article can be calculated according to the historical behavior sequence data of the user, the spreading force information of the article is added into the depth model, and sequence recommendation is helped to obtain better performance.