A linear unsupervised transfer learning by preservation of cluster-and-neighborhood data organization

Abstract

The paper has proposed a linear unsupervised transfer learning (LUTL). Therefore, a cost function has been introduced. In the cost function of the proposed LUTL, the aim is to minimize the difference between the distribution of the transformed source domain (SD) data and the distribution of the target domain (TD) data. In the proposed cost function, it is also targeted to preserve the local structures of the untransformed SD data. Three mechanisms have been proposed for the preservation of local structures in the untransformed SD data: (1) minimization of the distances between the data pairs that are similar to each other in the untransformed SD data, (2) preservation of the clusters emerged in the untransformed SD data and finally (3) their combination. The optimization problem has emerged as a nonlinear one. Two techniques have been introduced to obtain an approximation of the optimal weight matrix. Each technique guarantees to reach a local optimum, but no one guarantees to reach the global solution. While the first method is an iterative one, the second is a relaxed version of the optimization problem. The paper shows also experimentally that the proposed techniques overshadow the state-of-the-art methods.