Hashing refers to methods for embedding high dimensional data into a similarity-preserving low-dimensional Hamming space such that similar objects are ...
In this paper we base our semi-supervised hashing on linear discriminant analysis to learn discriminative binary codes, where hash functions are learned such ...
This paper base their semi-supervised hashing on linear discriminant analysis, where hash functions are learned such that labeled data are used to maximize ...
In this paper we base our semi-supervised hashing on linear discriminant analysis to learn discriminative binary codes, where hash functions are learned such ...
Hashing refers to methods for embedding high dimensional data into a similarity-preserving low-dimensional Hamming space such that similar objects are ...
This paper advances a new weakly supervised discrete discriminant hashing (WDDH) to ensure a more effective representation of data and better retrieval of ...
Mar 24, 2023 · Bibliographic details on Semi-supervised Discriminant Hashing.
Dec 2, 2022 · Our approach utilizes the graph neural network to exploit relationship between samples and conduct semantic infor- mation propagation, and ...
Jul 28, 2016 · In this paper, we propose the semi-supervised deep hashing (SSDH) approach, to perform more effective hash function learning by simultaneously preserving ...
Missing: Discriminant | Show results with:Discriminant
Based on this framework, we present three different semi-supervised hashing methods, including orthogonal hashing, non-orthogonal hashing, and sequential ...