Efficient k-nearest neighbors search in graph space

Abu-Aisheh, Zeina; Raveaux, Romain; Ramel, Jean-Yves

doi:10.1016/j.patrec.2018.05.001

Cited by 24 publications

(10 citation statements)

References 24 publications

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“…Abu-Aisheh proposed a multi-graph edit distance for KNN, the algorithm achieved faster time especially when the number of graphs is large [25]. Gou [29].…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

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“…Abu-Aisheh proposed a multi-graph edit distance for KNN, the algorithm achieved faster time especially when the number of graphs is large [25]. Gou [29].…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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show abstract

“…e feature set on the cloud server can also be calculated by X • A. en, the projection matrix A is transmitted to the We use the K-nearest neighbor (KNN) [26,27] algorithm to match features. KNN assumes the similarity between the new sample and available cases and puts the new case into the category that is most similar to the available categories.…”

Section: Detailed Design Of the Proposed Architecturementioning

confidence: 99%

Cooperative Cloud‐Edge Feature Extraction Architecture for Mobile Image Retrieval

2021

Complexity

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Mobile image retrieval greatly facilitates our lives and works by providing various retrieval services. The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. After preprocessing these captured image data and extracting features from these image data, the edge server uploads the extracted features to the cloud server. However, the feature extraction on the cloud server is noncooperative with the feature extraction on the edge server which cannot extract features effectively and has a lower image retrieval accuracy. For this, we propose a collaborative cloud-edge feature extraction architecture for mobile image retrieval. The cloud server generates the projection matrix from the image data set with a feature extraction algorithm, and the edge server extracts the feature from the uploaded image with the projection matrix. That is, the cloud server guides the edge server to perform feature extraction. This architecture can effectively extract the image data on the edge server, reduce network load, and save bandwidth. The experimental results indicate that this scheme can upload few features to get high retrieval accuracy and reduce the feature matching time by about 69.5% with similar retrieval accuracy.

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“…Abu-Aisheh proposed a multi-graph edit distance for KNN, the algorithm achieved faster time especially when the number of graphs is large [24]. Gou et al proposed a KNN algorithm by local mean representation [13].…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang,

2020

Preprint

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KNN classification is a query triggered yet improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

show abstract

Efficient k-nearest neighbors search in graph space

Cited by 24 publications

References 24 publications

KNN Classification with One-step Computation

KNN Classification with One-step Computation

Cooperative Cloud‐Edge Feature Extraction Architecture for Mobile Image Retrieval

KNN Classification with One-step Computation

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