LAUSR

In multi-source data analysis, the absence of data values or attributes is inevitably brought about by various influencing factors including environment, which results in the loss of knowledge to be conveyed by data. To solve the problem of missing data in multi-source data analysis, completion method for multiview missing data based on multi-manifold regularized non-negative matrix factorization was proposed in this paper. This method was based on the assumption of consistency of the multiview data and an algorithm of multi-manifold regularized non-negative matrix factorization is adopted to obtain homogeneous manifold and global clustering. On this basis, a multiview synergistic discrimination model is built of the non-missing view that referred to the Gaussian mixture model to pre-mark the clustering that the incremental missing data belonged to. Using the consistency of each view in the low-dimensional space, a prediction model of missing data at the specified view is established using the multiple linear regression technique to achieve accurate data completion under conditions of missing multi-attributes. Through the establishment of data filling model with three handling methods for missing values, namely CMMD-MNMF, FIMUS and Hot deck, the completion performance, clustering performance and classification performance of data sets including UCI, Flower17 and Flower102 are analyzed by simulation experiments. As shown in the results, the method of multi-view data missing completion is verified to be effective.