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Randomized algorithms for Kroncecker tensor decomposition and applications

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arXiv 2024年

作者： Ahmadi-Asl, Salman Rezaeian, Naeim de Almeida, André L.F. Liu, Yipeng Lab of Machine Learning and Knowledge Representation Innopolis University Innopolis420500 Russia Peoples’ Friendship University of Russia Moscow Russia Department of Teleinformatics Engineering Federal University of Ceara Fortaleza Brazil Chengdu611731 China

This paper proposes fast randomized algorithms for computing the Kronecker Tensor Decomposition (KTD). The proposed algorithms can decompose a given tensor into the KTD format much faster than the existing state-of-the-art algorithms. Our principal idea is to use the randomization framework to reduce computational complexity significantly. We provide extensive simulations to verify the effectiveness and performance of the proposed randomized algorithms with several orders of magnitude acceleration compared to the deterministic one. Our simulations use synthetics and real-world datasets with applications to tensor completion, video/image compression, image denoising, and image super-resolution. © 2024, CC BY.

关键词： Tensors

Smartphone gait fingerprinting models via genetic programming

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Smartphone gait fingerprinting models via genetic programmin...

Congress on Evolutionary Computation

作者： James Alexander Hughes Joseph Alexander Brown Adil Mehmood Khan Computer Science Department University of Western Ontario London Ontario Canada Innopolis Republic of Tatarstan Artificial Intelligence in Games Development Lab Innopolis University Russia Innopolis Republic of Tatarstan Machine Learning and Knowledge Representation Lab Innopolis University Russia

ISBN: (纸本)9781509006243

The idea of using the gait of a walking person as a biometric identification method has been seen in a number of proposed authentication methods, yet previous works focus on the addition of other authentication methods along with the gait, or require a stationary sensor attached to the hip of the user. This paper uses Genetic Programming to model an identification gait fingerprint for two users, whose walking data was recorded from the accelerometer in a commercially available phone. With the phone freely placed within a pocket, users moved without a fixed protocol at a normal, nonuniform pace. This design of data collection more closely matches the real world applications of such a method. The highly specialized Genetic Programming system with multiple modular enhancements was implemented to perform symbolic regression. The system was demonstrated to be robust to noise and was able to effectively model each dataset with high accuracy. It was also determined that a model could be generated for a subject's whole dataset from only a single step's worth of data. Top models were applied to other subject's data in order to evaluate the uniqueness of these mathematical models.

关键词： Data models Mathematical model Legged locomotion Genetic programming Computational modeling Data collection Standards

NONNEGATIVE TENSOR DECOMPOSITION VIA COLlabORATIVE NEURODYNAMIC OPTIMIZATION

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arXiv 2024年

作者： Ahmadi-Asl, Salman Leplat, Valentin Phan, Anh-Huy Cichocki, Andrzej Lab of Machine Learning and Knowledge Representation Innopolis University Innopolis Russia Skolkovo Institute of Science and Technology Center for Artificial Intelligence Technology Moscow Russia Innopolis University Innopolis Russia Systems Research Institute of Polish Academy of Science Warsaw Poland

This paper introduces a novel collaborative neurodynamic model for computing nonnegative Canonical Polyadic Decomposition (CPD). The model relies on a system of recurrent neural networks to solve the underlying nonconvex optimization problem associated with nonnegative CPD. Additionally, a discrete-time version of the continuous neural network is developed. To enhance the chances of reaching a potential global minimum, the recurrent neural networks are allowed to communicate and exchange information through particle swarm optimization (PSO). Convergence and stability analyses of both the continuous and discrete neurodynamic models are thoroughly examined. Experimental evaluations are conducted on random and real-world datasets to demonstrate the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Particle swarm optimization (PSO)

Randomized algorithms for computing the generalized tensor SVD based on the tensor product

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arXiv 2023年

作者： Ahmadi-Asl, Salman Rezaeian, Naeim Ugwu, Ugochukwu O. Lab of Machine Learning and Knowledge Representation Innopolis University Innopolis420500 Russia Center for Artificial Intelligence Technology Skolkovo Institute of Science and Technology Moscow Russia Peoples’ Friendship University of Russia Moscow Russia Department of Mathematics Colorado State University Fort Collins United States

This work deals with developing two fast randomized algorithms for computing the generalized tensor singular value decomposition (GTSVD) based on the tensor product (T-product). The random projection method is utilized to compute the important actions of the underlying data tensors and use them to get small sketches of the original data tensors, which are easier to handle. Due to the small size of the sketch tensors, deterministic approaches are applied to them to compute their GTSVD. Then, from the GTSVD of the small sketch tensors, the GTSVD of the original large-scale data tensors is recovered. Some experiments are conducted to show the effectiveness of the proposed approach. © 2023, CC BY.

关键词： Consensus algorithm

learning Fair representations through Uniformly Distributed Sensitive Attributes

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Learning Fair Representations through Uniformly Distributed ...

Secure and Trustworthy machine learning (SaTML), IEEE Conference on

作者： Patrik Joslin Kenfack Adín Ramírez Rivera Adil Mehmood Khan Manuel Mazzara Machine Learning and Knowledge Representation Lab Innopolis University Innopolis Russia Department of Informatics Digital Signal Processing and Image Analysis (DSB) group University of Oslo Oslo Norway School of Computer Science University of Hull Hull UK Institute of Software Development and Engineering Innopolis University Innopolis Russia

machine learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.

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Efficient Algorithms for Low Tubal Rank Tensor Approximation with Applications to Image Compression, Super-Resolution and Deep learning

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arXiv 2024年

作者： Ahmadi-Asl, Salman Rezaeian, Naeim Caiafa, Cesar F. de Almeida, André L.F. Lab of Machine Learning and Knowledge Representation Innopolis University Innopolis420500 Russia Peoples’ Friendship University of Russia Moscow Russia Instituto Argentino de Radioastronomia—CCT La Plata CONICET/CIC-PBA/UNLP Villa Elisa1894 Argentina Department of Teleinformatics Engineering Federal University of Ceara Fortaleza Brazil

In this paper we propose efficient randomized fixed-precision techniques for low tubal rank approximation of tensors. The proposed methods are faster and more efficient than the existing fixed-precision algorithms for approximating the truncated tensor SVD (T-SVD). Besides, there are a few works on randomized single-pass algorithms for computing low tubal rank approximation of tensors, none of them experimentally reports the robustness of such algorithms for low-rank approximation of real-world data tensors e.g., images and videos. The current single-pass algorithms for tensors are generalizations of those developed for matrices to tensors. However, the single-pass randomized algorithms for matrices have been recently improved and stabilized. Motivated by this progress, in this paper, we also generalize them to the tensor case based on the tubal product (T-product). We conduct extensive simulations to study the robustness of them compared with the existing single-pass randomized algorithms. In particular, we experimentally found that single-pass algorithms with the sketching parameters of equal sizes usually lead to ill-conditioned tensor least-squares problems and inaccurate results. It is experimentally shown that our proposed single-pass algorithms are robust in this sense. Numerical results demonstrate that under the same conditions (setting the same hyper-parameters), our proposed algorithms provide better performance. Three applications to image compression, super-resolution problem and deep learning are also presented. © 2024, CC BY.

关键词： Tensors

VAE-GAN Based Zero-shot Outlier Detection 2020

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VAE-GAN Based Zero-shot Outlier Detection

Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent Control

作者： Bekkouch Imad Ibrahim Dragoş Constantin Nicolae Adil Khan Syed Imran Ali Asad Khattak Machine Learning and Knowledge Representation Lab Institute of Data Science & AI Innopolis Tatarstan Russia Institutul de cercetări pentru Inteligenta Artificiala 'Mihai Draganescu' România Department of Computer Engineering Kyung Hee University Yongin-si South Korea College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets.

关键词： Deep learning Generative Models machine learning Outlier Detection

Corrections to “Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)”

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IEEE Access 2024年 12卷 70742-70742页

作者： Salman Ahmadi-Asl Stanislav Abukhovich Maame G. Asante-Mensah Andrzej Cichocki Anh Huy Phan Tohishisa Tanaka Ivan Oseledets CAIT Skolkovo Institute of Science and Technology (SKOLTECH) Moscow Russia Lab of Machine Learning and Knowledge Representation Innopolis University Innopolis Russia Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan

Presents corrections to the above mentioned paper.

关键词： Matrix decomposition Additives Tensors Object recognition Market research Approximation algorithms