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.
翻译:在这份简短的论文中,可以解释一个能够形成一个低维表层隐蔽表层的神经网络。神经网络可以作为自动解码器、分类器或两者的混合体来训练,并且为每个神经网络绘制不同的低维表层图。当它被训练为自动解码器时,数据固有的表层结构可以视觉化,而当它被训练为分类器时,其表层结构又受到这个概念的进一步制约,例如数据标签,因此可视化不仅是结构性的,而且也是概念性的。拟议的神经网络与许多多维减少模型大不相同,主要在于它执行受监督和不受监督的维度减少的能力。神经网络允许数据的多视角直观化,从而在数据分析方面给予更大的灵活性。这份文件得到了初步但直观的视觉化实验的支持。