  The t-distributed stochastic neighbor embedding (t- SNE) is a method for
interpreting high dimensional (HD) data by mapping each point to a low
dimensional (LD) space (usually two-dimensional). It seeks to retain the
structure of the data. An important component of the t-SNE algorithm is the
initialization procedure, which begins with the random initialization of an LD
vector. Points in this initial vector are then updated to minimize the loss
function (the KL divergence) iteratively using gradient descent. This leads
comparable points to attract one another while pushing dissimilar points apart.
We believe that, by default, these algorithms should employ some form of
informative initialization. Another essential component of the t-SNE is using a
kernel matrix, a similarity matrix comprising the pairwise distances among the
sequences. For t-SNE-based visualization, the Gaussian kernel is employed by
default in the literature. However, we show that kernel selection can also play
a crucial role in the performance of t-SNE. In this work, we assess the
performance of t-SNE with various alternative initialization methods and
kernels, using four different sets, out of which three are biological sequences
(nucleotide, protein, etc.) datasets obtained from various sources, such as the
well-known GISAID database for sequences of the SARS- CoV-2 virus. We perform
subjective and objective assessments of these alternatives. We use the
resulting t-SNE plots and k- ary neighborhood agreement (k-ANA) to evaluate and
compare the proposed methods with the baselines. We show that by using
different techniques, such as informed initialization and kernel matrix
selection, that t-SNE performs significantly better. Moreover, we show that
t-SNE also takes fewer iterations to converge faster with more intelligent
initialization.
