This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for node splitting during forest construction can handle rank-def...This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for 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.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees.A pseudo-leaf-splitting(PLS)algorithm is introduced to account for spatial relationships,which regularizes affinity measures and overcomes inconsistent leaf assign-ments.The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences.The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence.Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61876008 and 82071172Beijing Natural Science Foundation under Grant No.7192227the Research Center of Engineering and Technology for Digital Dentistry,the Ministry of Health.
文摘This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for 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.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees.A pseudo-leaf-splitting(PLS)algorithm is introduced to account for spatial relationships,which regularizes affinity measures and overcomes inconsistent leaf assign-ments.The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences.The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence.Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.
