The traditional Internet of Brain Things’ big data information filtering method ignores the extraction of big data features, the filtering effect, and the effect of denoising processing on the simulation results, resulting in low filtering accuracy and poor performance. An effective information filtering and mining algorithm for the Internet of Brain Things based on support vector machine (SVM) is proposed. First, the model construction and feature extraction of the Internet of Brain Things’ big data system are carried out. The correlation feature extraction is performed on the effective information features; the correlation factors of the effective information data are sorted; the main feature quantity of the relevance degree is extracted; and the filter non-association is designed. The information is reasonably filtered; all data are processed, converted to the same interval for processing; data protocol is implemented; and data effective information feature mining is implemented based on the SVM algorithm. The simulation results show that the algorithm is effective for filtering big data and has high precision and superior performance, which shows good application value.

Collaborative filtering (CF) has been widely used in various recommender systems, but often suffers from the problem of data sparsity which dramatically degrades the recommendation performance. In this paper, we propose a co-training style semi-supervised CF approach towards the task of rating prediction, which exploits a few observed ratings in conjunction with copious unobserved ones to reduce sparsity. In each round of co-training iterations, our approach utilizes two different neighborhood-based recommenders, each of which labels the unobserved data for the other recommender; in particular, the most informative unobserved examples are actively selected for labeling, and then the labeling confidence is estimated through validating the influence of the labeling of unobserved examples on the observed ones. Experiments results on the three datasets demonstrate that our approach can effectively exploit unobserved data to improve CF predictions, and achieves better performance than other counterparts.

In this study, the problem of two‐dimensional (2D) direction of arrival estimation for uncorrelated signals with an L‐shaped array is discussed. The estimated cross covariance matrix (CCM) between the sample data along the x ‐axis and the z ‐axis is constructed. The elevation angle of the signal is obtained by finding the sparse coefficients of the estimated CCM based on the discussion of statistical probability distribution of the sample data error. Once the elevation angle is obtained, with the non‐coupling between the elevation angle and the azimuth angle, the azimuth angle of each source is estimated by the eigenvalue decomposition of the conjugate transpose matrix of the estimated CCM. The proposed algorithm has better estimation performance in the situation of low signal‐to‐noise‐ratio cases but also the estimated azimuth and elevation angles are paired automatically. The performance of the proposed method is compared with some existing schemes and the effectiveness of the method is demonstrated by computer simulations.