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Computational Visual Media

Article Title

Unsupervised random forest for affinity estimation

Keywords

affinity estimation, forest-based metric, unsupervised clustering forest, pseudo-leaf-splitting (PLS)

Abstract

This paper presents an unsupervised cluste-ring random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion usedfor node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.

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