The task of dimensionality reduction and visualization of high-dimensional
datasets remains a challenging problem since long. Modern high-throughput
technologies produce newer high-dimensional datasets having multiple views with
relatively new data types. Visualization of these datasets require proper
methodology that can uncover hidden patterns in the data without affecting the
local and global structures within the data. To this end, however, very few
such methodology exist, which can realise this task. In this work, we have
introduced a novel unsupervised deep neural network model, called NeuroDAVIS,
for data visualization. NeuroDAVIS is capable of extracting important features
from the data, without assuming any data distribution, and visualize
effectively in lower dimension. It has been shown theoritically that
neighbourhood relationship of the data in high dimension remains preserved in
lower dimension. The performance of NeuroDAVIS has been evaluated on a wide
variety of synthetic and real high-dimensional datasets including numeric,
textual, image and biological data. NeuroDAVIS has been highly competitive
against both t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform
Manifold Approximation and Projection (UMAP) with respect to visualization
quality, and preservation of data size, shape, and both local and global
structure. It has outperformed Fast interpolation-based t-SNE (Fit-SNE), a
variant of t-SNE, for most of the high-dimensional datasets as well. For the
biological datasets, besides t-SNE, UMAP and Fit-SNE, NeuroDAVIS has also
performed well compared to other state-of-the-art algorithms, like Potential of
Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) and the siamese
neural network-based method, called IVIS. Downstream classification and
clustering analyses have also revealed favourable results for
NeuroDAVIS-generated embeddings.

本文介绍了一种名为 NeuroDAVIS 的无监督深度神经网络模型，用于高维数据的降维和可视化，该模型在大量合成和真实高维数据集上表现出了竞争力，特别是在保留数据大小、形状和本地全局结构方面。