I was learning about t-SNE when I was told that t-SNE retains the structure of the data in the embeddings.
What exactly does this mean ?
How does the algorithm achieve this ?
So far I have understood this ->
t-SNE is an unsupervised learning algorithm that is used for dimensionality reduction and visualization of high dimentioanal data. The algorithm works by measuring the similarity between one point and all the other points using the t-curve. The width of the curve is dependant on the density of the cluster the point belongs to. t-SNE retains the structure of the initial data.
My question is what does it mean by retaining the structure of the data? Shouldn't there be some loss in the structure of the data seeing as it is transformed into a lower dimensional space ? Also what does "strucutre of the data mean"? Please ask for any further details that are required.