Unsupervised Feature Selection using Orthogonal Encoder-Decoder Factorization

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, we incorporate orthogonality constraints and local structure preservation restrictions into the objective function.

Information Sciences, 2024

M. Mozafari | S. A. Seyedi | R. Pir Mohammadiani | F. Akhlaghian Tab

Multi-Label Feature Selection with Global and Local Label Correlation

This paper proposes 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 L2,1-norm regularization is formulated, and an alternating optimization-based iterative algorithm is designed to obtain the sparse coefficients for multi-label feature selection.

Expert Systems with Applications, 2024

M. Faraji | S. A. Seyedi | F. Akhlaghian Tab | R. Mahmoodi

Deep Asymmetric Nonnegative Matrix Factorization for Graph Clustering

This paper proposes a graph-specific Deep NMF 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).

Pattern Recognition, 2024

A. Hajiveiseh | S. A. Seyedi | F. Akhlaghian Tab

Link Prediction by Adversarial Nonnegative Matrix Factorization

We proposed a novel link prediction method based on adversarial NMF, which reconstructs a sparse network by an efficient adversarial training algorithm. Unlike the conventional NMF methods, our model considers potential test adversaries beyond the pre-defined bounds and provides a robust reconstruction with good generalization power. Besides, to preserve the local structure of a network, we use the common neighbor algorithm to extract the node similarity and apply it to low-dimensional latent representation.

Knowledge-based Systems, 2023

R. Mahmoodi | S. A. Seyedi | F. Akhlaghian Tab | A. Abdollahpouri

Self-Supervised Semi-Supervised Nonnegative Matrix Factorization for Data Clustering

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.

Pattern Recognition, 2023

J. Chavoshinejad | S. A. Seyedi | F. Akhlaghian Tab | N. Salahian

Elastic Adversarial Deep Nonnegative Matrix Factorization for Matrix Completion

This paper proposes 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, we address the challenges mentioned above by perturbing the inputs in DNMF with an elastic loss which is intercalated and adapted between Frobenius and L2,1 norms. This model not only dispenses with adversarial DNMF generation but also is robust towards a mixture of multiple attacks to attain improved accuracy.

Information Sciences, 2023

S. A. Seyedi | F. Akhlaghian Tab | A. Lotfi | N. Salahian | J. Chavoshinejad

Deep Autoencoder-like NMF with Contrastive Regularization and Feature Relationship Preservation

This paper proposes the Deep Autoencoder-like NMF with Contrastive Regularization and Feature Relationship preservation (DANMF-CRFR) to address the data representation challenges. Inspired by contrastive learning, this deep model is able to learn discriminative and instructive deep features while adequately enforcing the local and global structures of the data to its decoder and encoder components. Meanwhile, DANMF-CRFR also imposes feature correlations on the basis matrices during feature learning to improve part-based learning capabilities.

Expert Systems with Applications, 2023

N. Salahian | F. Akhlaghian Tab | S. A. Seyedi | J. Chavoshinejad

In this paper, we propose a self-paced multi-label learning with diversity (SPMLD) which aims to cover diverse labels with respect to its learning pace. In addition, the proposed framework is applied to an efficient correlation-based multi-label method. The non-convex objective function is optimized by an extension of the block coordinate descent algorithm. Empirical evaluations on real-world datasets with different dimensions of features and labels imply the effectiveness of the proposed predictive model.

Asian Conference on Machine Learning, 2019

S. A. Seyedi | S. Ghodsi | F. Akhlaghian Tab | M. Jalili | P. Moradi

In this paper, a dynamic weakly supervised factorization is proposed to learn a classifier using NMF framework and partially supervised data. Also, a label propagation mechanism is used to initialize the label matrix factor of NMF. Besides a graph based method is used to dynamically update the partially labeled data in each iteration. This mechanism leads to enriching the supervised information in each iteration and consequently improves the classification performance.

Artificial Intelligence and Signal Processing Conference (AISP), 2017

S. A. Seyedi | P. Moradi | F. Akhlaghian Tab