1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to: read a dataset that includes a plurality of observation vectors; read a label set, wherein the label set defines permissible values for a target variable, wherein a value of the permissible values of the target variable is defined for a subset of the plurality of observation vectors; compute an affinity matrix using a kernel function and the plurality of observation vectors; compute a diagonal matrix by summing each row of the computed affinity matrix, wherein the sum of each row is stored in a diagonal of the row with zeroes in remaining positions of the row; compute a normalized distance matrix using the computed affinity matrix and the computed diagonal matrix; define a label matrix using the value of the target variable of each observation vector of the plurality of observation vectors (a) initialize a classification matrix as the defined label matrix; (b) compute a converged classification matrix, wherein the converged classification matrix defines a label probability for each permissible value defined in the label set for each observation vector of the plurality of observation vectors, wherein the classification matrix is converged using F(t+1)=αSF(t)+(1−α)Y, where F(t+1) is a next classification matrix, α is a relative weighting value, S is the normalized distance matrix defined as S=D −1/2 WD −1/2 , where W is the computed affinity matrix and D is the computed diagonal matrix, F(t) is the classification matrix, Y is the label matrix defined as Y ik =1 if x i is labeled as y i =k; otherwise, Y ik =0, where x i is an observation vector of the plurality of observation vectors, i=1,. .. , n, n is a number of vectors of the plurality of observation vectors, k=1,. .. , c, and c is a number of permissible values of the label set, and t is an iteration number, wherein the classification matrix is converged when a second predefined number of iterations of computations of F(t+1)=αSF(t)+(1−α)Y is complete; (c) for each observation vector, update the value of the target variable based on a maximum label probability value identified from the converged classification matrix; a predefined number of times, compute a distance vector that defines a distance value between each pair of the plurality of observation vectors using a distance function applied to only the converged classification matrix; select a number of observation vectors from the dataset that have minimum values for the distance value; request that a user provide a label for each of the selected observation vectors; receive a response to the request from the user for each of the selected observation vectors; update the value of the target variable for each of the selected observation vectors with the received response; and repeat operations (a) to (c); and after the predefined number of times, output the value of the target variable for each observation vector of the plurality of observation vectors to a second dataset.