Multi-view outlier detection for potential relationship capture with paired comparison avoidance

A multi-view outlier detection method, comprising:

• defining first and second data structures in memory of a host computer;
• transforming original multi-view samples into the first data structure as a tensor representation to form a set of multi-view tensors stored in the first data structure;
• vectorizing in the second data structure, each tensor in the first data structure, the vectorization producing a transformed sample matrix;
• constructing an objective function for low-rank representation learning for the sample matrix, and calculating an optimal representation coefficient matrix and error matrix, which minimize a value of the objective function;
• calculating outlier scores of all samples according to the representation coefficient matrix and the error matrix obtained in the vectorization, so as to output outlier labels of all samples;
• and,
• creating a file for storage in fixed storage of the host computer, the file including a set of outlier scores of all of the samples in the vectorized form of the data structure, the detected outliers capturing possible relationships among multiple views of the tensor representation while avoiding a paired comparison between the views.