Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMap have
demonstrated impressive visualization performance on many real-world datasets. One …

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states,
trace development lineages, and reconstruct the spatial organization of cells. However …

Recurrent Neural Networks (RNNs) are artificial neural network models that are well-suited
for pattern classification tasks whose inputs and outputs are sequences. The importance of …

We present a new technique called “t-SNE” that visualizes high-dimensional data by giving
each datapoint a location in a two or three-dimensional map. The technique is a variation of …

In this research we address the problem of high-dimensional in hyperspectral images, which
may contain rare/anomaly vectors introduced in the subspace observation that we wish to …

Techniques for multidimensional scaling visualize objects as points in a low-dimensional
metric map. As a result, the visualizations are subject to the fundamental limitations of metric …

We propose a new dimensionality reduction method, the elastic embedding (EE), that
optimises an intuitive, nonlinear objective function of the low-dimensional coordinates of the …

Dendrobium Sw., as a traditional herb and function food with over 1500 years of history,
shows a significant effect in improving immunity and fatigue resistance. However, due of …

For humans, it is usually easier to make statements about the similarity of objects in relative,
rather than absolute terms. Moreover, subjective comparisons of objects can be based on a …

Meta-learning has emerged as an efficient approach for constructing target models based
on support sets. For example, the meta-learned embeddings enable the construction of …