Trustworthy dimension reduction for visualization different data sets

A new nonlinear dimension reduction (DR) method which is called Trustworthy Stochastic Proximity Embedding (TSPE) is introduced in this paper to visualize different types of data sets. TSPE overcome the main shortcomings of the DR by sending the false neighbour points to the correct locations, and preserving the neighbourhood relation to the true neighbours, which are inside the local neighbourhood. The visualization of our proposed method displays the trustworthy, useful and meaningful colours, where the objects of the image can be easily distinguished. The performances of TSPE and 20 dimension reduction methods are compared, and the efficiency of the proposed method in both visualization accuracy and computational cost is shown. The results showed the ability of our method in preserving neighbourhood relation, where they revealed more interested information. In real data set, the efficiency of the visualization of tensor images data sets by TSPE might help the specialist to make a good decision about a patient’s treatment. The comparison with experimental data set, as three dimensions of curved cylinder, showed the ability of TSPE to unfold this complex data set efficiently whilst preserving most information of the original data set.