Hashing with Graphs

By Wei David Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang. International Conference on Machine Learning, 2011

1,070 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d8e4936a405bdf58830be2559e9a39e533a0ed42

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Zero-shot Neural Retrieval via Domain-targeted Synthetic Query Generation (2020, methodology): “Fast approximate nearest neighbour search is possible for dense vectors through techniques described in Liu et al. (2011).”
Pairwise Teacher-Student Network for Semi-Supervised Hashing (2019, methodology): “Learning-tohash methods can be divided into three categories: unsupervised hashing [7, 17], supervised hashing [16, 14] and semi-supervised hashing [28, 27, 20].”
Hashing for Similarity Search: A Survey (2014, methodology): “spectral hashing [135] E LE HD CC + BB + BU kernelized spectral hashing [37] S, E KE HD CC + BB + BU Hypergraph spectral hashing [153], [89] S CL HD CC + BB + BU Topology preserving hashing [145] E LI HD CC + CCD + BB + BU hashing with graphs [83] S KE HD CC + BB ICA Hashing [35] E LI, KE HD CC + BB”
Circulant Binary Embedding (2014, methodology): “Introduction Embedding input data in binary spaces is becoming popular for efficient retrieval and learning on massive data sets (Li et al., 2011; Gong et al. , 2013a; Raginsky & Lazebnik , 2009; Gong et al. , 2012; Liu et al., 2011).”
Linear cross-modal hashing for efficient multimedia search (2013, methodology): “Such a phenomenon has also been discussed in [18, 38].”
HashNet: Deep Learning to Hash by Continuation (2017, background): “Typical learning criteria include reconstruction error minimization [30, 12, 16] and graph learning[36, 24].”
Hashing with binary autoencoders (2015, methodology): “Using ITQ (Gong et al., 2013) produces also good results (occasionally better but generally somewhat worse than AGH), and is a simpler and faster option if so desired.”
Scalable Graph-based Bug Search for Firmware Images (2016, background): “The graph is a complex structure, and thus directly indexing or hashing graphs at scale is still a challenging problem [36].”
Multiscale Quantization for Fast Similarity Search (2017, background): “Binary embedding based approaches [36, 24, 18, 13, 17, 25] focus on learning short binary codes, and can be searched efficiently in Hamming space.”
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search (2019, methodology): “Representative unsupervised hashing methods include iterative quantization (ITQ) [53], spectral hashing (SH) [22], discrete graph hashing (DGH) [54], spherical Hashing (SpH) [55], anchor graph hashing (AGH) [56], and stochastic generative hashing (SGH) [57].”
Deep Supervised Discrete Hashing (2017, methodology): “Similar to [8, 11, 19, 23], we use a subset of 195,834 images which are associated with the 21 most frequent concepts.”
Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (2018, methodology): “For the graph construction component, the time complexity of constructing anchor graph is OðdmnÞ [31].”
Structure-Preserving Binary Representations for RGB-D Action Recognition (2016, methodology): “In our experiments, we compare the proposed method against seven general hashing algorithms including KSH [58], BRE [60], MLH [61], LSH [56], SpH [57], AGH [68], PCAH [69], BSSC [53] and RBM [70].”
A Survey of Multi-View Representation Learning (2016, unknown): “The data within each modality are first encoded into a low-rank representation using the idea of anchor graph [159] and then hash functions for each modality are learned to map each modality’s low-rank space into a shared Hamming space.”
Discrete Graph Hashing (2014, methodology): “These include the unsupervised methods, e.g., Iterative Quantization [9], Isotropic Hashing [14], Spectral Hashing [38] [37], and Anchor Graph Hashing [23], the semi-supervised methods, e.g., Weakly-Supervised Hashing [25], and the supervised methods, e.g., Semantic Hashing [30], Binary Reconstructi”
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes (2013, methodology): “Studies on compact encoding are roughly in two streams, categorized by their ways of distance computation: Hamming-based methods like locality sensitive hashing (LSH) [9, 1, 28] and others [31, 14, 29, 5, 19, 16, 15], and lookup-based methods like vector quantization [7] and product quantization [10”
Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization (2016, methodology): “[23] proposed an anchor graph based hashing method to learn appropriate compact codes with a feasible computational cost.”