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Mar 12, 2016 · We develop a practical and efficient algorithm for NIBH based on column generation that scales well to large datasets. A range of experimental ...
Abstract. We develop a scalable algorithm to learn bi- nary hash codes for indexing large-scale datasets. Near-isometric binary hashing (NIBH) is a data-.
Bibliographic details on Near-Isometric Binary Hashing for Large-scale Datasets.
[PDF] Isometric Mapping Hashing - Griffith Research Online
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Abstract. Hashing is a popular solution to Approximate Nearest Neigh- bor (ANN) problems. Many hashing schemes aim at preserving the Eu-.
May 26, 2016 · In this paper, we present a hashing scheme that preserves the geodesic distance and use a feasible out-of-sample method to generate the binary ...
Hashing is a popular solution to Approximate Nearest Neighbor (ANN) problems. Many hashing schemes aim at preserving the Euclidean distance of the original ...
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To solve those problems, we propose a novel unsupervised deep hashing framework to learn compact binary codes, which takes the quadruplet forms as input units, ...
Hashing Algorithms for Large-Scale Learning · pdf icon · hmtl icon · Ping Li, Anshumali ... Readers: Everyone. Near-Isometric Binary Hashing for Large-scale ...
We propose a novel framework for the deterministic construction of linear, near-isometric embeddings of a finite set of data points. Given a set of training ...
Abstract. Hashing is an important tool in large-scale machine learning. Unfortunately, current data-dependent hashing algorithms are not robust to small ...