This paper presents a nonlinear multidimensional scaling model, called kernelized fourth quantification theory, which is an integration of kernel techniques and the fourth quantification theory. The model can deal with the problem of mineral prediction without defining a training area. In mineral target prediction, the pre-defined statistical cells, such as grid cells, can be implicitly transformed using kernel techniques from input space to a high-dimensional feature space, where the nonlinearly separable clusters in the input space are expected to be linearly separable. Then, the transformed cells in the feature space are mapped by the fourth quantification theory onto a low-dimensional scaling space, where the scaled cells can be visually clustered according to their spatial locations. At the same time, those cells, which are far away from the cluster center of the majority of the scaled cells, are recognized as anomaly cells. Finally, whether the anomaly cells can serve as mineral potential target cells can be tested by spatially superimposing the known mineral occurrences onto the anomaly cells. A case study shows that nearly all the known mineral occurrences spatially coincide with the anomaly cells with nearly the smallest scaled coordinates in one-dimensional scaling space. In the case study, the mineral target cells delineated by the new model are similar to those predicted by the well-known WofE model.