Dimensionality Reduction for $k$-means Clustering
arXiv:2007.13185v1
Originality Synthesis-oriented
AI Analysis
This work addresses dimensionality reduction for clustering, which is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackles the problem of reducing dimensions for k-means clustering to achieve provably accurate approximations, presenting four randomized algorithms based on feature selection and feature extraction.
We present a study on how to effectively reduce the dimensions of the $k$-means clustering problem, so that provably accurate approximations are obtained. Four algorithms are presented, two \textit{feature selection} and two \textit{feature extraction} based algorithms, all of which are randomized.