1. A method to visualize high-dimensional data, comprising: creating, with a processor, an n-dimensional primarily-visualized image of the high-dimensional data, wherein n is less than a number of dimensions of the high-dimensional data, by using any of: a dimension reduction according to a dimension of the primarily-visualized image, a dimension reduction according to characteristics of the data, or a dimension reduction according to a form of the primarily-visualized image; selecting the high-dimensional data in an area of the primarily-visualized image; creating an (n+m)-dimensional secondarily-visualized image of the selected high-dimensional data, wherein m is greater than or equal to one; manipulating the secondarily-visualized image by enlarging, reducing, or rotating the secondarily-visualized image, thereby creating a new visualization having characteristics of the selected high-dimensional data that are more clearly visualized than in the secondarily-visualized image; and converting the manipulated secondarily-visualized image into an n-dimensional image, such that the new visualization is preserved in the n-dimensional image; wherein n is less than a number of dimensions of the high-dimensional data, by using feature extraction, feature selection, or a combination thereof; wherein the feature extraction includes any of: a non-negative matrix factorization (NMF), an isomap, a local linear embedding (LLE), and linear discriminant analysis (LDA); and wherein the feature selection includes any of: feature selection based on information gain, and feature selection based on mutual information.