Mixing autoencoder with classifier: conceptual data visualization
This provides a flexible tool for data analysts to visualize data from multiple perspectives, though it appears incremental as it combines existing autoencoder and classifier approaches.
The paper tackles the problem of visualizing data by proposing a neural network that creates low-dimensional topological maps, which can be trained as an autoencoder for unsupervised structural visualization or as a classifier for supervised conceptual visualization, with preliminary experiments demonstrating this capability.
In this short paper, a neural network that is able to form a low dimensional topological hidden representation is explained. The neural network can be trained as an autoencoder, a classifier or mix of both, and produces different low dimensional topological map for each of them. When it is trained as an autoencoder, the inherent topological structure of the data can be visualized, while when it is trained as a classifier, the topological structure is further constrained by the concept, for example the labels the data, hence the visualization is not only structural but also conceptual. The proposed neural network significantly differ from many dimensional reduction models, primarily in its ability to execute both supervised and unsupervised dimensional reduction. The neural network allows multi perspective visualization of the data, and thus giving more flexibility in data analysis. This paper is supported by preliminary but intuitive visualization experiments.