William G. Finn

Artificial Intelligence · 2009–2011

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom William G. Finn trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ted Chang
2. R. Golden
3. Mikhail Belkin
4. S. Mancell

Papers from William G. Finn's perspective

Ordered by how strongly the researchers William G. Finn trusts endorse each paper. Same corpus, a different order for every researcher.

1. A global geometric framework for nonlinear dimensionality reduction.
2. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
3. The Elements of Statistical Learning
4. Methods of information geometry
5. Geometrical foundations of asymptotic inference
6. Introduction to Statistical Pattern Recognition
7. Statistical Pattern Recognition
8. From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space
9. Multi-Instance Dimensionality Reduction
10. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization

Their papers

• Information-Geometric Dimensionality Reduction (2011) — 34 citations
• Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high‐dimensional objects (2009) — 49 citations