Processing of Multi-valued Attributes Based on Sparse Matrix

Multi-value attribute has always been a difficult problem to deal with in machine learning. Most models are unique for data format matching. When it is multi-value, most models cannot be used directly. At the same time, a large number of multi-valued attributes will be encountered in the construction of medical model. These attributes often represent that patients have multiple symptoms.. The processing methods of multivalued attributes can be roughly divided into two categories, one is through data preprocessing, the other is through algorithm pattern matching. The solution to medical multi-valued attributes in this paper is mainly through preprocessing, from the perspective of multi-valued attribute representation and projection. The process is to use sparse matrix to represent multivalued data, convert it to high-dimensional space, and then project it back to one dimension to complete the processing of such data.