Abstract To solve the out-of-sample problem of t-distributed stochastic neighbor embedding(t-SNE) analysis method and overcome the unfeasibility of manually adjusting the involved parameters in practice, a linear fix-point neighbor embedding(LFNE) analysis method is proposed based on a fix-point optimization algorithm. Based on t-SNE, the linear projection matrix is introduced to reveal the underlying structure of data manifold in LFNE. Then, the penalty function is built by minimizing the Kullback-Leibler divergence of original space and subspace. Furthermore, the efficiency and the robustness of LFNE optimization are improved by the fix-point optimization algorithm. The proposed method is evaluated on artificial synthetic data and COIL-20 database. Experimental results demonstrate the better effectiveness of visualization by LFNF.
2015, Vol. 28 
