科研之友
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摘要
Document collections are often stored as sets of sparse, high-dimensional feature vectors. Performing dimensionality reduction (DR) on such high-dimensional datasets for the purposes of visualization presents algorithmic and qualitative challenges for existing DR techniques. We propose the Q-SNE algorithm for dimensionality reduction of document data, combining the scalable probability-based layout approach of BH-SNE with an improved component to calculate approximate nearest neighbors, using the query-based APQ approach that exploits an impact-ordered inverted file. We provide thorough experimental evidence that Q-SNE yields substantial quality improvements for layouts of large document collections with commensurate speed. Our experiments were conducted with six real-world benchmark datasets that range up to millions of documents and terms, and compare against three alternatives for nearest neighbor search and five alternatives for dimensionality reduction.
- 出版日期2015-2-20
