1. A pattern recognition method, comprising: providing a data processing device to implement the following steps: accessing multiple sets of training data, each set of training data having at least one of a true example of a pattern to be recognized or a false example of a pattern to be recognized; arranging said multiple sets of data into a data matrix U in an electronic memory constituting a data store, wherein each set of training data is arranged as a separate column in data matrix U and data matrix U is defined as Uε d×n , where is the set of real numbers and d×n define the dimensions of data matrix U; defining an intrinsic graph G to label specific features of most interest in the sets of training data, wherein G={U,W}, labels that identify favorable features that are features characteristic of the pattern to be recognized are added to the sets of training data in U, each column of U represents a vertex, W is a similarity matrix and each element of similarity matrix W measures the similarity between vertex pairs; defining a penalty graph G to label specific features of least interest in the sets of training data, wherein G ={U, W }, labels that identify unfavorable features that are features not characterisic of the pattern to be recognized are added to the sets of training data in U, W is a dissimilarity matrix, and each element of dissimilarity matrix W measures unfavorable relationships between said vertex pairs; defining an intrinsic diagonal matrix D, wherein D=[D ij ] and D ii =Σ j=1 n W ij ; defining an intrinsic Laplacian matrix L, wherein L=D−W; defining a penalty diagonal matrix D , wherein D =[ D ij ] and D ii =Σ j=1 n = W ij ; defining a penalty Laplacian matrix L , wherein L = D − W ; defining a basis matrix V, wherein Vε d×r and basis matrix V is to hold basis examples of the sets of training data, the basis examples being a reduction of the sets of training data into simplified representations that highlight distinguishing characteristics of the pattern to be recognized; defining a feature matrix X, wherein Xε r×n feature matrix X is to hold features values to construct an approximation of U from basis matrix V; incorporating the label information of intrinsic graph G and penalty graph G into the construction of basis matrix V and features matrix X by defining a measure of the compactness of intrinsic graph G by the weighted sum of squared distances defined as Σ i<j n W ij ∥x i −x j ∥ 2 =Tr(XLX T ), wherein x i is the i-th column of X and x j is the j-th column of X; defining a measure of the separability of penalty graph G by the weighted sum of squared distances defined as Σ i<j n W ij ∥x i −x j ∥ 2 =Tr(X L X T ) wherein x i is the i-th column of X and x j is the j-th column of X; defining F (1) (V,X) as an objective of NMF (nonnegative matrix factorization), F (1) (V,X) being proportional to F (1) (V,X)=½∥U−VX∥ F 2 ; defining F (2) (X) as an objective of graph embedding, F (2) (X) being proportional to ratio Tr ⁡ ( XLX T ) Tr ⁡ ( X ⁢ L _ ⁢ X T ) ; deriving an SNMF (supervised nonnegative matrix factorization) objective from a sum of F (1) (V,X) and F (2) (X); populating basis matrix V and feature matrix X by solving the derived SNMF objective through iterative multiplicative updates; and separating recognizable patterns within the basis examples of basis matrix V into distinct pattern classifications using a data classifier, these pattern classifications of the basis examples being deemed the recognized patterns of the sets of training data; wherein: said pattern recognition method is a face detection method; each of said set of training data is a distinct training image; the favorable features labeled in intrinsic graph G identify regions within each distinct training image that are to be focused upon when defining the basis examples; the unfavorable features labeled in intrinsic graph G identify regions within each distinct training image that are of least interest when defining the basis examples; at least one of said distinct pattern classifications is a face pattern classification; and a received test image is tested for the existence of a face by determining a basis test sample from the received test image, submitting the basis test sample to the data classifier, and if the data classifier identifies the basis test sample as belonging to the face pattern classification, then deeming the received test image as having a rendition of a face.