Unsupervised adaptive hashing based on feature clustering

Yuan, Tongtong; Deng, Weihong; Hu, Jiani; An, Zhanfu; Tang, Yinan

doi:10.1016/j.neucom.2018.10.015

Cited by 16 publications

(9 citation statements)

References 24 publications

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“…In this section, we verify the effectiveness of our proposed method by carrying out experiments of image retrieval on three real-world image datasets including CIFAR-10 [35], YouTube Faces [36], and MNIST [37]. We compare SCUH with six well-behaved hashing algorithms, including ITQ [17], LGHSR [43], SH [38], SGH [34], AGH [42], DGH [41], PCAH [18], and FCH [30].…”

Section: Methodsmentioning

confidence: 99%

“…Principal component analysis hashing (PCAH [18]) also acquires compact binary codes and preserve the similarities among neighbors. Later on, feature clustering hashing (FCH [30]) further considers the unbalanced variance distribution and uncertain similarity relations, when learning the projection matrix as in PCAH and PCA-ITQ. To model manifold structure, scalable graph hashing (SGH [19]) and anchor graph hashing (AGH [31]) are devised to capture the geometrical structure within dataset in a Hamming space and show the advantages of unsupervised graph hashing.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Zhao

Luo

2022

IEEE Access

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“…Duan et al [45] treated projection and quantization as a whole and solved it by using the minimal reconstruction bias of signals. Yuan et al [46] adopted k-means to quantize codes on each feature that is generated by using feature clustering.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Hashing With Sparse Matrix Factorization

Liu

Zhang

2020

IEEE Trans. Neural Netw. Learning Syst.

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Hashing offers a desirable and effective solution for efficiently retrieving the nearest neighbors from large-scale data because of its low storage and computation costs. One of the most appealing techniques for hashing learning is matrix factorization. However, most hashing methods focus only on building the mapping relationships between the Euclidean and Hamming spaces and, unfortunately, underestimate the naturally sparse structures of the data. In addition, parameter tuning is always a challenging and head-scratching problem for sparse hashing learning. To address these problems, in this article, we propose a novel hashing method termed adaptively sparse matrix factorization hashing (SMFH), which exploits sparse matrix factorization to explore the parsimonious structures of the data. Moreover, SMFH adopts an orthogonal transformation to minimize the quantization loss while deriving the binary codes. The most distinguished property of SMFH is that it is adaptive and parameter-free, that is, SMFH can automatically generate sparse representations and does not require human involvement to tune the regularization parameters for the sparse models. Empirical studies on four publicly available benchmark data sets show that the proposed method can achieve promising performance and is competitive with a variety of state-of-the-art hashing methods.

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“…Once the centroid is obtained, the newly extracted features are the distance of any object in the dataset in respect to the k centroids. K-means clustering was used for dimensionality reduction in [15] for image classification and dubbed as Feature Clustering Hashing method. In this work, we have implemented K-means clustering straightforward as proposed in [19] where the number of clusters is provided as new labels used as the new features.…”

Section: K-means Clusteringmentioning

confidence: 99%

“…K-Means clustering method is seldom used for dimensionality reduction, though recently published paper in[15] K-Means was used for hashing clustering to reduce feature dimensionality for image classification. This published work has explained the difference between image clustering and feature clustering for image classification purpose.…”

mentioning

confidence: 99%

Dimensionality Reduction using PCA and K-Means Clustering for Breast Cancer Prediction

Jamal

Handayani²,

Septiandri³

et al. 2018

LKJITI

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Breast cancer is the most important cause of death among women. A prediction of breast cancer in early stage provides a greater possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumor whether it was a harmful malignant tumor or un-harmful benign tumor. In this paper, two algorithms of machine learning, namely Support Vector Machine and Extreme Gradient Boosting technique will be compared for classification purpose. Prior to the classification, the number of data attribute will be reduced from the raw data by extracting features using Principal Component Analysis. A clustering method, namely K-Means is also used for dimensionality reduction besides the Principal Component Analysis. This paper will present a comparison among four models based on two dimensionality reduction methods combined with two classifiers which applied on Wisconsin Breast Cancer Dataset. The comparison will be measured by using accuracy, sensitivity and specificity metrics evaluated from the confusion matrices. The experimental results have indicated that the K-Means method, which is not usually used for dimensionality reduction can perform well compared to the popular Principal Component Analysis.

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Unsupervised adaptive hashing based on feature clustering

Cited by 16 publications

References 24 publications

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Adaptive Hashing With Sparse Matrix Factorization

Dimensionality Reduction using PCA and K-Means Clustering for Breast Cancer Prediction

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