1. Summary of knowledge
This section introduces the singular solution decomposition of a matrix, which essentially transforms a set of orthogonal bases in the row space into a set of orthogonal bases in the column space. The diagram is as follows
2. Singular value decomposition SVD
3. Learning and understanding
SVD is very important and has some connection with the least squares method. This part of the content is mainly about applying and connecting the previously learned content. In fact, the uses of SVD go far beyond what we have introduced. It can also reduce some calculations, perform image transformation, etc.