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Explainable recommendations with nonnegative matrix factorization
Explicable recommendation system is proved to be conducive to improving the persuasiveness of the recommendation system, enabling users to trust the...
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Trilateration-based indoor localization utilizing the matrix completion method by probabilistic matrix factorization and stochastic gradient
This research presents an enhanced indoor positioning system using Received Signal Strength Indicator (RSSI) values and probabilistic matrix...
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Double-ConvMF: probabilistic matrix factorization with user and item characteristic text
In today’s information-rich society, the importance of recommender systems for matching items and customers is increasing day by day. The development...
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Binary Matrix Factorization Discretization
Binary Matrix Factorization can be used at the core of many data analysis pipelines. It is used for clustering items, categorical characteristics of...
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Deep manifold matrix factorization autoencoder using global connectivity for link prediction
Link prediction aims to predict missing links or eliminate spurious links by employing known complex network information. As an unsupervised linear...
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DMFNet: deep matrix factorization network for image compressed sensing
Due to its outstanding performance in image processing, deep learning (DL) is successfully utilized in compressed sensing (CS) reconstruction....
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Denoising matrix factorization for high-dimensional time series forecasting
The matrix factorization method (MF) has gained widespread popularity in recent years as an effective technique for handling high-dimensional time...
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An Ensemble Model for Combining Deep Matrix Factorization and Image-Based Recommendation Systems
Recommender systems are widely used in many domains, especially in E-commerce. It can be used for attracting users by recommending appropriate...
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Multi-criteria recommendation system based on deep matrix factorization and regression techniques
Recommender systems have emerged as effective solutions for managing information overload by providing personalized predictions. Nowadays a deep...
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Robust Dual-Graph Regularized Deep Matrix Factorization for Multi-view Clustering
The matrix factorization approaches have been widely applied for multi-view clustering since they can effectively explore complementary information...
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A semantic-consistency asymmetric matrix factorization hashing method for cross-modal retrieval
Hashing methods have recently received widespread attention due to their flexibility and effectiveness for cross-modal retrieval tasks. However,...
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Binary Orthogonal Non-negative Matrix Factorization
We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested...
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Error Graph Regularized Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been received much attention and widely applied to data mining by various researchers. It is believed that...
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Cauchy balanced nonnegative matrix factorization
Nonnegative Matrix Factorization (NMF) plays an important role in many data mining and machine learning tasks. Standard NMF uses the Frobenius norm...
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An algorithm of non-negative matrix factorization with the nearest neighbor after per-treatments
Clustering is a hot topic in machine learning. For high dimension data, nonnegative matrix factorization (NMF) is a crucial technology in clustering....
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Adaptive graph nonnegative matrix factorization with the self-paced regularization
Nonnegative matrix factorization (NMF) is a popular approach to extract intrinsic features from the original data. As the nonconvexity of NMF...
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Adaptive graph regularized non-negative matrix factorization with self-weighted learning for data clustering
In general, fully exploiting the local structure of the original data space can effectively improve the clustering performance of nonnegative matrix...
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Link prediction using deep autoencoder-like non-negative matrix factorization with L21-norm
Link prediction aims to predict missing links or eliminate spurious links and anticipate new links by analyzing observed network topological...
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Efficient FPGA implementation for sound source separation using direction-informed multichannel non-negative matrix factorization
Sound source separation (SSS) is a fundamental problem in audio signal processing, aiming to recover individual audio sources from a given mixture. A...
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Deep non-negative matrix factorization with edge generator for link prediction in complex networks
AbstractLink prediction aims to infer missing links or predict future links based on observed topology or attribute information in the network. Many...
