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Fast Nearest-Neighbor Classifier based on Sequential Analysis of Principal Components
Ch. 7. P. 73–80.
Sokolova A., Savchenko A.
In this paper we improve the speed of the nearest neighbor classifiers of a set of points based on sequential analysis of high-dimensional feature vectors. Each input object is associated with a sequence of principal component scores of aggregated features extracted by deep neural network. The number of components in each element of this sequence is dynamically chosen based on explained proportion of total variance for the training set.We propose to process the next element with higher explained variance only if the decision for the current element is unreliable. This reliability is estimated by matching of the ratio of the minimum distance and all other distances with a certain threshold. Experimental study for face recognition with the Labeled Faces in the Wild and YouTube Faces datasets demonstrates the decrease of running time up to 10 times when compared to conventional instance-based learning.
Keywords: face recognition principal component analysis Sequential analysishigh-dimensional features deep neural networknearest neighbor classifier
Publication based on the results of:
In book
Vol. 11832. , Cham: Springer, 2019.
Minnigulova A., Dragoy O., Arutiunian V., European Child and Adolescent Psychiatry 2026
Communication deficits in Autism Spectrum Disorder (ASD) involve impairments in both verbal and nonverbal domains, potentially associated with altered brain network connectivity related to language, attention, and social cognition systems. This study investigated functional connectivity patterns among the Default Mode Network (DMN), Salience Network (SN), Dorsal Attention Network (DAN), and Language Network (LN) in male children ...
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Sokolova A., Savchenko A., , in: 2023 IX International Conference on Information Technology and Nanotechnology (ITNT).: IEEE, 2023. P. 1–5.
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Added: June 13, 2023
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Added: May 29, 2023
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Added: October 31, 2022
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