Kernel t-distributed stochastic neighbor embedding
This work addresses a domain-specific problem for researchers and practitioners in machine learning, particularly in classification tasks involving kernel methods, and is incremental as it extends the existing t-SNE algorithm.
The paper tackles the problem of preserving pairwise distances in non-Euclidean metrics when reducing high-dimensional data to low-dimensional spaces, resulting in a kernelized t-SNE algorithm that shows neater clustering of points across different classes in datasets.
This paper presents a kernelized version of the t-SNE algorithm, capable of mapping high-dimensional data to a low-dimensional space while preserving the pairwise distances between the data points in a non-Euclidean metric. This can be achieved using a kernel trick only in the high dimensional space or in both spaces, leading to an end-to-end kernelized version. The proposed kernelized version of the t-SNE algorithm can offer new views on the relationships between data points, which can improve performance and accuracy in particular applications, such as classification problems involving kernel methods. The differences between t-SNE and its kernelized version are illustrated for several datasets, showing a neater clustering of points belonging to different classes.