Author: Xing EP,Ng AY,Jordan MI, et al.Distance Metric Learning,with Application to Clustering with Side-information [C]//International Conference on Neural Information Processing Systems, 2002.
Input
X: data
S: similarity constraints (in the form of a pairwise-similarity matrix)
D: disimilarity constraints (in the form of a pairwise-disimilarity matrix)
A: initial distance metric matrix
w: a weight vector originated from similar data (see paper) upper bound of constraint C1 (the sum of pairwise distance bound)
maxiter: maximum iterations
Output
A: the solution of distance metric matrix
converged: indicator of convergence
iters: iterations passed until convergence
中文：
X：数据
S：相似性约束（以成对相似性矩阵的形式）
D：相异性约束（以成对相异矩阵的形式）
A：初始距离度量矩阵
w：来自类似数据的权重向量（见论文）
t：约束 C1 的上限（成对距离的总和）
maxiter：最大迭代次数
iter_projection_new2(X, S, D, A, w, t, maxiter)