12 results on '"Akhlaghian Tab, Fardin"'
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2. Link prediction by adversarial Nonnegative Matrix Factorization
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Mahmoodi, Reza, Seyedi, Seyed Amjad, Akhlaghian Tab, Fardin, and Abdollahpouri, Alireza
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- 2023
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3. Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging
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Mollazade, Kaveh, Omid, Mahmoud, Akhlaghian Tab, Fardin, Kalaj, Yousef Rezaei, Mohtasebi, Seyed Saeid, and Zude, Manuela
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- 2013
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4. Elastic adversarial deep nonnegative matrix factorization for matrix completion.
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Seyedi, Seyed Amjad, Akhlaghian Tab, Fardin, Lotfi, Abdulrahman, Salahian, Navid, and Chavoshinejad, Jovan
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MATRIX decomposition , *NONNEGATIVE matrices , *DATA structures , *LEAD time (Supply chain management) , *GENERALIZATION - Abstract
In recent years, matrix completion has attracted a lot of attention. Conventional matrix completions are shallow and ineffective when dealing with complex data structures. Researchers have recently tried to incorporate deep structures into matrix completion; however, considerable challenges still exist. Most matrix completion methods may fail to work effectively in the presence of limited observations. To enhance the generalization of the reconstruction, adversarial methods are proposed that attempt to fool models by providing deceptive input. The aim is to develop an adversarial training algorithm that resists attacks in a deep model, thus at the same time leading to enhancing the generalization. Therefore, in this paper, we propose an elastic adversarial training to design a high-capacity Deep Nonnegative Matrix Factorization (DNMF) model with proper discovery latent structure of the data and enhanced generalization abilities. In other words, the challenges mentioned above are addressed by perturbing the inputs in DNMF with an elastic loss which is intercalated and adapted between Frobenius and ℓ 2 , 1 norms. This model not only dispenses with adversarial DNMF generation but also is robust towards a mixture of multiple attacks to attain improved accuracy. Extensive simulations show that the proposed approach outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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- 2023
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5. A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between users.
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Ramezani, Mohsen, Akhlaghian Tab, Fardin, Abdollahpouri, Alireza, and Abdulla Mohammad, Mahmud
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RECOMMENDER systems , *CONFIDENCE , *SCALABILITY , *SPECULATION , *FORECASTING - Abstract
• Extracting dominant opinion patterns of frequent favored-unfavored items. • Assigning users to the patterns to detect high confidence relations between them. • An assigned user to some pattern mediates between users of those patterns. • For each user, an impact value is calculated as the role in rating prediction. • Experimental results on four datasets show the proper performance of our method. In this paper, a new collaborative filtering method is proposed based on finding similar users directly and indirectly to overcome sparsity challenge. Moreover, selecting these users through extracting dominant opinion patterns leads to tackling scalability. In this method, frequent opinions between users are extracted to be used as dominant patterns. Then, users corresponding to the same dominant pattern are considered as direct similar users. Direct similar users who have seen more items and have corresponded to more than one pattern are regarded as reference users. Each reference user mediates between users who may have no/few commonly seen items and they are considered as indirect similar users. Utilizing indirect similar user helps the method to predict opinion of query user about items, which have not been seen by any direct similar user before. Clearly, indirect users are selected based on speculation using available information about direct users' preferences. Thus, the effect of indirect users is considered to be stricter than that of direct users on the final prediction step. Experiments conducted on MovieLens small, MovieLens 100 k, MovieLens 1 M, and Jester datasets showed that the proposed method outperforms the previously introduced methods, especially on sparse data. [ABSTRACT FROM AUTHOR]
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- 2021
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6. A highly robust and secure image watermarking based on classification and visual cryptography.
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Fatahbeygi, Ali and Akhlaghian Tab, Fardin
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VISUAL cryptography , *IMAGE recognition (Computer vision) , *DIGITAL watermarking , *SUPPORT vector machines , *ROBUST control - Abstract
• A novel and secure block-based image watermarking algorithm is proposed. • The security is enhanced by using visual cryptography. • Considering stability of image edges increases the robustness of the presented method. • The proposed method is completely imperceptible. In this paper a new robust image watermarking algorithm based on blocks classification and visual cryptography (VC) is presented. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and the support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth classes. The VC technique is used to generate two image shares: A master share that is constructed according to the block classification results and an owner share generated by using the master share together with a binary watermark. To verify the ownership of the image, the watermark can be retrieved by stacking the master share and the owner share. By skipping blocks which are not robust against attacks, the robustness of our proposed watermarking method is significantly improved. Our method is completely imperceptible because the watermark pattern is concealed without modifying the original host image. [ABSTRACT FROM AUTHOR]
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- 2019
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7. Multi-label feature selection with global and local label correlation.
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Faraji, Mohammad, Seyedi, Seyed Amjad, Akhlaghian Tab, Fardin, and Mahmoodi, Reza
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FEATURE selection , *MATRIX decomposition , *NONNEGATIVE matrices , *SHARED workspaces - Abstract
In various application domains, high-dimensional multi-label data has become more prevalent, presenting two significant challenges: instances with high-dimensional features and a large number of labels. In the context of multi-label feature selection, the objective is to choose a subset of features from a given set that is highly pertinent for predicting multiple labels or categories associated with each instance. However, certain characteristics of multi-label classification, such as label dependencies and imbalanced label distribution, have often been overlooked although they hold valuable insights for designing effective multi-label feature selection algorithms. In this paper, we propose a feature selection model which exploits explicit global and local label correlations to select discriminative features across multiple labels. In addition, by representing the feature matrix and label matrix in a shared latent space, the model aims to capture the underlying correlations between features and labels. The shared representation can reveal common patterns or relationships that exist across multiple labels and features. An objective function involving L 2 , 1 -norm regularization is formulated, and an alternating optimization-based iterative algorithm is designed to obtain the sparse coefficients for multi-label feature selection. The proposed method was evaluated on 14 real-world multi-label datasets using six evaluation metrics, through comprehensive experiments. The results indicate its effectiveness, surpassing that of several representative methods. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Deep asymmetric nonnegative matrix factorization for graph clustering.
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Hajiveiseh, Akram, Seyedi, Seyed Amjad, and Akhlaghian Tab, Fardin
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MATRIX decomposition , *NONNEGATIVE matrices , *DIRECTED graphs , *UNDIRECTED graphs , *REPRESENTATIONS of graphs , *HIERARCHICAL clustering (Cluster analysis) - Abstract
Graph clustering is a fundamental technique in machine learning that has widespread applications in various fields. Deep Nonnegative Matrix Factorization (DNMF) was recently emerged to cope with the extraction of several layers of features, and it has been demonstrated to achieve remarkable results on unsupervised tasks. While DNMF has been applied for analyzing graphs, the effectiveness of the current DNMF approaches for graph clustering is generally unsatisfactory: these methods are intrinsically data representation models, and their objective functions do not capture cluster structures, also ignores direction which is crucial in the directed graph clustering problems. To overcome these downsides, this paper proposes a graph-specific DNMF model based on the Asymmetric NMF which can handle undirected and directed graphs. Inspired by hierarchical graph clustering and graph summarization approaches, the Deep Asymmetric Nonnegative Matrix Factorization (DAsNMF) is introduced for the directed graph clustering problem. In a pseudo-hierarchical clustering setting, DAsNMF decomposes the input graph to extract low-level to high-level node representations and graph representations (summarized graphs). In addition, the asymmetric cosine and PageRank-based similarities are imposed on the proposed model to preserve the local and global graph structures. The learning process is formulated as a unified optimization problem to jointly train representation learning model and clustering model. The extensive experimental studies validate the effectiveness of the proposed method on directed graphs. • A graph specific deep matrix factorization model is proposed for graph clustering. • The direction-aware model can learn hierarchical structures and graph summarization. • Two Asymmetric similarities are utilized to preserve the local and global structures. • A unified optimization approach is provided to solve the proposed objective function. • Experiments demonstrate the effectiveness of the method in directed graph clustering. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Self-supervised semi-supervised nonnegative matrix factorization for data clustering.
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Chavoshinejad, Jovan, Seyedi, Seyed Amjad, Akhlaghian Tab, Fardin, and Salahian, Navid
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MATRIX decomposition , *NONNEGATIVE matrices , *SUPERVISED learning , *SOURCE code , *MATHEMATICAL optimization , *PROBLEM solving - Abstract
• A self-supervised semi-supervised NMF method is proposed for data clustering. • We define an effective semi-supervised NMF model with attractive and repulsive interactions. • An ensemble of semi-supervised NMFs is designed to model a self-supervised one. • The self-supervised model generates pseudo supervisory signals to boost semi-supervised learning. • Experimental results demonstrate the effectiveness of the proposed method in semi-supervised clustering tasks. Semi-supervised nonnegative matrix factorization exploits the strengths of matrix factorization in successfully learning part-based representation and is also able to achieve high learning performance when facing a scarcity of labeled data and a large amount of unlabeled data. Its major challenge lies in how to learn more discriminative representations from limited labeled data. Furthermore, self-supervised learning has been proven very effective at learning representations from unlabeled data in various learning tasks. Recent research works focus on utilizing the capacity of self-supervised learning to enhance semi-supervised learning. In this paper, we design an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S4NMF) in a semi-supervised clustering setting. The S4NMF directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. In an iterative process, this self-supervisory information will be fed back to the proposed model to boost semi-supervised learning and form more distinct clusters. The proposed iterative algorithm is used to solve the given problem, which is defined as an optimization problem with a well-formulated objective function. In addition, the theoretical and empirical analyses investigate the convergence of the proposed optimization algorithm. To demonstrate the effectiveness of the proposed model in semi-supervised clustering, we conduct extensive experiments on standard benchmark datasets. The source code for reproducing our results can be found at https://github.com/ChavoshiNejad/S4NMF. [ABSTRACT FROM AUTHOR]
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- 2023
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10. Enhancing link prediction through adversarial training in deep Nonnegative Matrix Factorization.
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Mahmoodi, Reza, Seyedi, Seyed Amjad, Abdollahpouri, Alireza, and Akhlaghian Tab, Fardin
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MATRIX decomposition , *NONNEGATIVE matrices , *SPARSE graphs , *REGULARIZATION parameter , *DEEP learning , *FORECASTING , *FEATURE extraction - Abstract
Link prediction is a fundamental problem in complex network analysis, aimed at predicting missing or forthcoming connections. Recent research investigates the potential of Nonnegative Matrix Factorization (NMF) models in reconstructing sparse networks and using deep NMF to uncover the hierarchical structure. Deep models have demonstrated remarkable performance in various domains, but they are susceptible to overfitting, especially on the limited training data. This paper proposes a novel Link Prediction using Adversarial Deep NMF (LPADNMF) to enhance the generalization of network reconstruction in sparse graphs. The main contribution is the introduction of an adversarial training that incorporates a bounded attack on the input, leveraging the L 2 , 1 norm to generate diverse perturbations. This adversarial training aims to improve the model's robustness and prevent overfitting, particularly in scenarios with limited training data. Additionally, the proposed method incorporates first and second-order affinities as input to capture higher-order dependencies and encourage the extraction of informative features from the network structure. To further mitigate overfitting, a smooth L 2 regularization is applied to the model parameters. To optimize the proposed model effectively, we utilize a majorization-minimization algorithm that efficiently updates the perturbation and latent factors in an iterative manner. Our findings demonstrate that the proposed model not only has the ability to uncover and learn complex structures but also possesses generalization capabilities. This method demonstrates superior performance compared to state-of-the-art methods across eight networks. In comparison to the best-performing approach, LPADNMF exhibited improvements of 2.36% in AUC, 4.67% in Precision, 2.21% in Recall, and 3.73% in F-Measure. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. A novel extreme learning machine based kNN classification method for dealing with big data.
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Shokrzade, Amin, Ramezani, Mohsen, Akhlaghian Tab, Fardin, and Abdulla Mohammad, Mahmud
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MACHINE learning , *K-nearest neighbor classification , *DATA mining , *BIG data , *NEAREST neighbor analysis (Statistics) , *CLASSIFICATION , *DATA mapping - Abstract
• Some ELMs are used in parallel as experts to map data into discriminative space. • The training data are easily grouped into some clusters in the new feature space. • An index is calculated per group to help finding corresponding groups of a sample. • A tree is used to find corresponding groups of a sample and applying kNN on it. • The method is evaluated on 4 big datasets in absence and presence of noise. kNN algorithm, as an effective data mining technique, is always attended for supervised classification. On the other hand, the previously proposed kNN finding methods cannot be considered as efficient methods for dealing with big data. As there is daily generated and expanded big datasets on different online and offline servers, the efficient methods for such data must be introduced to find kNN. Moreover, massive amounts of data contain more noise and imperfection data samples that significantly increase the need for a robust kNN finding method. In this paper, a new fast and robust kNN finding framework is introduced to deal with the big datasets. In this method, a group of most relevant data samples to an input data sample are detected and the original kNN method is applied on them for finding the final nearest neighbors. The main goal of this method is dealing with the big datasets in an accurate, fast, and robust manner. Here, the training data samples of each label are grouped into some partitions based on the output of some mini-classifiers (i.e. ELM classifier). In fact, the behavior of the mini-classifiers is the basis of partitioning the training data samples. These mini-classifiers are trained using non-overlapping subsets of the training set in the form of each mini-classifier a subset. Here, an index is calculated for each partition to make the corresponding partition finding faster using a tree structure in which each partition index is fallen into a leaf. Then, outputs of the mini-classifiers for an input test sample are used to find the corresponding group of most relevant data samples to the input data sample on the tree. Experimental results indicate that the proposed method has better performance in most cases and comparable performance on other cases of original and noisy big data problems. [ABSTRACT FROM AUTHOR]
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- 2021
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12. Unsupervised feature selection using orthogonal encoder-decoder factorization.
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Mozafari, Maryam, Seyedi, Seyed Amjad, Pir Mohammadiani, Rojiar, and Akhlaghian Tab, Fardin
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FEATURE selection , *FACTORIZATION , *MACHINE learning , *MATRIX decomposition , *NONNEGATIVE matrices - Abstract
Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more efficient and interpretable models. Current embedded UFS methods primarily rely on self-representation or pseudo-supervised feature selection approaches to address redundancy and irrelevant feature issues, respectively. Nevertheless, there is currently a lack of research showcasing the fusion of these two approaches. This paper proposes the Orthogonal Encoder-Decoder factorization for unsupervised Feature Selection (OEDFS) model, combining the strengths of self-representation and pseudo-supervised approaches. This method draws inspiration from the self-representation properties of autoencoder architectures and leverages encoder and decoder factorizations to simulate a pseudo-supervised feature selection approach. To further enhance the part-based characteristics of factorization, orthogonality constraints and local structure preservation restrictions are incorporated into the objective function. The optimization process is based on the multiplicative update rule, ensuring efficient convergence. To assess the effectiveness of the proposed method, comprehensive experiments are conducted on 14 datasets and compare the results with eight state-of-the-art methods. The experimental results demonstrate the superior performance of the proposed approach in terms of UFS efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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