Learning to Hash for Recommendation with Tensor Data

2015 ◽  
pp. 292-303
Author(s):  
Qiyue Yin ◽  
Shu Wu ◽  
Liang Wang
Keyword(s):  
Learning To Hash ◽  
Tensor Data

scholarly journals Deep Hash with Improved Dual Attention for Image Retrieval

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 285
Author(s):  
Wenjing Yang ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li ◽  
Anyu Du
Keyword(s):  
Image Retrieval ◽  
Contextual Information ◽  
Quantization Error ◽  
Feature Representation ◽  
Semantic Features ◽  
Average Value ◽  
Hash Code ◽  
Feature Information ◽  
Learning To Hash ◽  
Study Designs

Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for image retrieval (DHIDA), which chiefly has the following contents: (1) this paper introduces the improved dual attention mechanism (IDA) based on the ResNet18 pre-trained module to extract the feature information of the image, which consists of the position attention module and the channel attention module; (2) when calculating the spatial attention matrix and channel attention matrix, the average value and maximum value of the column of the feature map matrix are integrated in order to promote the feature representation ability and fully leverage the features of each position; and (3) to reduce quantization error, this study designs a new piecewise function to directly guide the discrete binary code. Experiments on CIFAR-10, NUS-WIDE and ImageNet-100 show that the DHIDA algorithm achieves better performance.

Semi-supervised non-negative Tucker decomposition for tensor data representation

2021 ◽  
Author(s):  
YuNing Qiu ◽  
GuoXu Zhou ◽  
XinQi Chen ◽  
DongPing Zhang ◽  
XinHai Zhao ◽  
...  
Keyword(s):  
Data Representation ◽  
Tucker Decomposition ◽  
Tensor Data

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

scholarly journals Learning to Hash with Graph Neural Networks for Recommender Systems

2020 ◽  
Author(s):  
Qiaoyu Tan ◽  
Ninghao Liu ◽  
Xing Zhao ◽  
Hongxia Yang ◽  
Jingren Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recommender Systems ◽  
Graph Neural Networks ◽  
Learning To Hash

A Generalized Graph Regularized Non-Negative Tucker Decomposition Framework for Tensor Data Representation

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yuning Qiu ◽  
Guoxu Zhou ◽  
Yanjiao Wang ◽  
Yu Zhang ◽  
Shengli Xie
Keyword(s):  
Data Representation ◽  
Tucker Decomposition ◽  
Tensor Data

Three-dimensional conjugate gradient inversion of magnetotelluric impedance tensor data

2011 ◽  
Vol 22 (3) ◽  
pp. 386-395 ◽  
Author(s):  
Changhong Lin ◽  
Handong Tan ◽  
Tuo Tong
Keyword(s):  
Conjugate Gradient ◽  
Three Dimensional ◽  
Impedance Tensor ◽  
Tensor Data
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