An Efficient Hyperdimensional Computing Paradigm for Face Recognition

Yasser, Mohammad; Hussain, Khaled F.; Ali, Shahzad

doi:10.1109/access.2022.3197668

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…One property that we wanted to evaluate for hyperdimensional computing classification models is how well these models can generalize information [76]. This experiment aimed to show how much data a classification model needs, to capture its patterns.…”

Section: Partial Datamentioning

confidence: 99%

Classification using Hyperdimensional Computing: A~Review~with~Comparative Analysis

Vergés,

Heddes,

Nunes

et al. 2023

Preprint

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Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is an emerging and promising paradigm for cognitive computing. At its core, HD/VSA is characterized by its distinctive approach to representing information using high-dimensional random vectors. The recent surge in research within this field gains momentum from its remarkable computational efficiency and ability to excel in few-shot learning scenarios. Nonetheless, the current literature is deficient in providing a comprehensive comparative analysis of various methodologies since each method uses a different benchmark to evaluate its performance. This gap obstructs the monitoring of the field's state-of-the-art advancements and acts as a significant barrier to its overall progress. To address this gap, this review not only offers a conceptual overview of the latest literature but also introduces a comprehensive comparative study of HD/VSA classification techniques. Our exploration starts with an overview of the strategies proposed to encode information as high-dimensional vectors. These vectors serve as integral components in the construction of classification models. Furthermore, we evaluate diverse classification methods as proposed in the existing literature. This evaluation encompasses techniques such as retraining and regenerative training to augment the model's performance. To conclude our study, we present a comprehensive empirical study. This study serves as an in-depth analysis, systematically comparing various HD/VSA classification approaches using two benchmarks, the first being a set of seven popular datasets used in HD/VSA and the second consisting of 121 datasets extracted from the UCI Machine Learning repository.To facilitate future research on classification with HD/VSA, we open-sourced the benchmarking and the implementations of the methods we review. Our findings yield significant insights. Firstly, encodings based on key-value pairs emerge as optimal choices, boasting superior accuracy while maintaining high efficiency. Secondly, iterative adaptive methods demonstrate remarkable efficacy, potentially complemented by a regenerative strategy depending on the specific problem. Moreover, we show how HD/VSA is able to generalize while training with a limited number of training instances. Lastly, we demonstrate the robustness of HD/VSA methodologies by subjecting the model memory to a large number of bit-flips. Our results illustrate that the model's performance remains reasonably stable until the occurrence of 40\% of bit flips, where the model's performance is drastically degraded. Overall, we have performed a thorough performance evaluation on different methods and have observed a positive trend in both evaluation and execution time.

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Section: Partial Datamentioning

confidence: 99%

Classification using Hyperdimensional Computing: A~Review~with~Comparative Analysis

Vergés,

Heddes,

Nunes

et al. 2023

Preprint

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“…. As the result of transformation (6), each descriptor E e v  of the etalon database gets the parameter k of the group number (basket or cluster).…”

Section: Data Transformation Based On the Indexed Structure For High-...mentioning

confidence: 99%

“…Such methods are the most effective for recognizing images of the fixed structure [2], [3]. The KP descriptor is the vector of size of 64…512 binary components that are the approximation for the fragment of the image brightness function [4], [6]. The traditional image classifier is based on the metric criteria of the relevance of the view "set-set" between the images of the recognized object and the etalon and on the optimization of the value of relevance for the etalon database [2].…”

Section: Introductionmentioning

confidence: 99%

Tools for Fast Metric Data Search in Structural Methods for Image Classification

et al. 2022

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The article proposes a new classification method based on implementing the high-speed search tools for the indexed data structure created on the etalon set of features, which has significant advantages in processing speed compared to the traditional approaches. The classifier is represented as twostage processing, where at the first stage the class for the separate object descriptor is determined, and at the second stage, the resulting class of the object is determined based on the obtained set of local solutions. The developed method is based on the preliminary construction of the indexed hash structures for the set of descriptors of the base of the etalon images. Implementing the hash representation allows for increasing the speed of identification or classification of visual objects. A comparative experiment with the traditional method of voting has been conducted, where the linear search for the nearest descriptor has been implemented for the identification without the use of prior creation of the indexed hash representation of the etalons. In the experiment, we have gained in processing speed for the developed method compared to the traditional over 10 times. The gain in processing speed increases proportionally with the number of the etalons and the number of the descriptors in the descriptions. The experiment has shown that the efficiency of the method can be enhanced by varying the values of its parameters and adapting to the properties of the data.

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Comparative Analysis of Similarity Methods in High-Dimensional Vectors: A Review

Yasser,

Hussain,

Ali

2023

2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS)

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An Efficient Hyperdimensional Computing Paradigm for Face Recognition

Cited by 4 publications

References 36 publications

Classification using Hyperdimensional Computing: A~Review~with~Comparative Analysis

Classification using Hyperdimensional Computing: A~Review~with~Comparative Analysis

Tools for Fast Metric Data Search in Structural Methods for Image Classification

Comparative Analysis of Similarity Methods in High-Dimensional Vectors: A Review

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