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A scalable unsupervised feature merging approach to efficient dimensionality reduction of high-dimensional visual data

Liu, Lingqiao; Wang, Lei


To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix-based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an efficient way to reduce the dimensionality,...[Show more]

CollectionsANU Research Publications
Date published: 2013
Type: Conference paper
Source: Proceedings of the IEEE International Conference on Computer Vision
DOI: 10.1109/ICCV.2013.374


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