  The self-organizing map (SOM) is an unsupervised artificial neural network
that is widely used in, e.g., data mining and visualization. Supervised and
semi-supervised learning methods have been proposed for the SOM. However, their
teacher labels do not describe the relationship between the data and the
location of nodes. This study proposes a landmark map (LAMA), which is an
extension of the SOM that utilizes several landmarks, e.g., pairs of nodes and
data points. LAMA is designed to obtain a user-intended nonlinear projection to
achieve, e.g., the landmark-oriented data visualization. To reveal the learning
properties of LAMA, the Zoo dataset from the UCI Machine Learning Repository
and an artificial formant dataset were analyzed. The analysis results of the
Zoo dataset indicated that LAMA could provide a new data view such as the
landmark-centered data visualization. Furthermore, the artificial formant data
analysis revealed that LAMA successfully provided the intended nonlinear
projection associating articular movement with vertical and horizontal movement
of a computer cursor. Potential applications of LAMA include data mining,
recommendation systems, and human-computer interaction.
