Paper Info
Title
Fast Nearest-Neighbor Classifier Based On Sequential Analysis Of Principal Components
Abstract
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.
Year
DOI
Venue
2019
10.1007/978-3-030-37334-4_7
ANALYSIS OF IMAGES, SOCIAL NETWORKS AND TEXTS, AIST 2019
Keywords
DocType
Volume
Sequential analysis, Principal component analysis, High-dimensional features, Deep neural network, Nearest neighbor classifier, Face recognition
Conference
11832
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Anastasiia D. Sokolova100.34
Andrey V. Savchenko200.68