Scalable k-means for large-scale clustering

Ming, Yuewei; Zhu, En; Wang, Mao; Liu, Qiang; Liu, Xinwang; Yin, Jianping

doi:10.3233/ida-173795

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…With or without use of supervised data labels, datadependent hashing methods can be separated into two groups: unsupervised [16]- [19] and supervised [20]- [24]. Compared with the latter, the unsupervised can work well in more broad cases by aid of some extra method, such as clustering [25], [26]. To name a few, spectral hashing [16], [27] seeks compact binary codes of data-points so that the Hamming distance between binary codes correlates with semantic similarity.…”

Section: Introductionmentioning

confidence: 99%

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Zhao

Luo

2022

IEEE Access

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Learning based hashing approaches have achieved considerable success in large-scale image retrieval due to the query effectiveness and efficiency. However, most studies highly rely on supervised knowledge like data labels, thus might fail in unsupervised setting. To address this issue, we propose a selfcollaborative unsupervised hashing method (SCUH), which jointly learns hashing function and virtual labels in a self-collaborative manner. Different from existing unsupervised counterparts, SCUH treats sample features and the learned virtual labels as two groups of different features in a fair view. This results in two groups of transformation matrices, which have proven beneficial for capturing diversity information of different features to better advance binary codes. Moreover, we devise an alternate optimization algorithm to solve SCUH, in which each sub-problem has a closed-form solution as a byproduct. Experiments of image retrieval on three datasets show that SCUH performs favorably over eight comparative methods including unsupervised counterparts.

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Section: Introductionmentioning

confidence: 99%

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Zhao

Luo

2022

IEEE Access

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A New Adaptive Hybrid Mutation Black Widow Clustering Based Data Partitioning for Big Data Analysis

Ravikumar¹,

Kavitha

2021

Wireless Pers Commun

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AI-Based Secure Construction of University Information Services Platform

Wang

2022

Security and Communication Networks

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The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university’s secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively.

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Scalable k-means for large-scale clustering

Cited by 4 publications

References 15 publications

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

A New Adaptive Hybrid Mutation Black Widow Clustering Based Data Partitioning for Big Data Analysis

AI-Based Secure Construction of University Information Services Platform

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