Papers Matrix Factorization / Decomposition
“Matrix Factorization / Decomposition” 태그가 달린 논문 16편 · 필터 해제
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods have the following three problems: 1) they…
Community DetectionContrastive LearningGraph EmbeddingMatrix Factorization / Decomposition+1FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources…
Distributed ComputingFederated LearningLow-rank compressionMatrix Factorization / Decomposition+1Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction
Background subtraction refers to extracting the foreground from an observed video, and is the fundamental problem of various applications. There are two kinds of popular methods to deal with background separation, namely…
Low-Rank Matrix CompletionMatrix CompletionMatrix Factorization / DecompositionVideo Background SubtractionFast Rank-1 NMF for Missing Data with KL Divergence
We propose a fast non-gradient-based method of rank-1 non-negative matrix factorization (NMF) for missing data, called A1GM, that minimizes the KL divergence from an input matrix to the reconstructed rank-1 matrix. Our m…
Matrix Factorization / DecompositionMissing ValuesJoint Matrix Decomposition for Deep Convolutional Neural Networks Compression
Deep convolutional neural networks (CNNs) with a large number of parameters require intensive computational resources, and thus are hard to be deployed in resource-constrained platforms. Decomposition-based methods, ther…
Efficient Neural NetworkMatrix Factorization / DecompositionNeural Network CompressionAccurate and fast matrix factorization for low-rank learning
In this paper, we tackle two important problems in low-rank learning, which are partial singular value decomposition and numerical rank estimation of huge matrices. By using the concepts of Krylov subspaces such as Golub…
Matrix Factorization / DecompositionRiemannian optimizationNuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks. In this paper, we argue that existing PSs are inefficient for tasks that exhibit non-uniform parameter acces…
BIG-bench Machine LearningKnowledge Graph EmbeddingsManagementMatrix Factorization / Decomposition+1Low-rank Convex/Sparse Thermal Matrix Approximation for Infrared-based Diagnostic System
Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis…
Breast Cancer DetectionClusteringDefect DetectionDiagnostic+1Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser
With the wide deployment of digital image capturing equipment, the need of denoising to produce a crystal clear image from noisy capture environment has become indispensable. In this article, a novel type-2 fuzzy-based f…
DenoisingImage DenoisingLow-Rank Matrix CompletionMatrix Completion+1Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix. Our key insight is to formulate rank reduction as a mean-fi…
Matrix Factorization / DecompositionEfficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries
Bayesian low-rank matrix factorization techniques have become an essential tool for relational data analysis and matrix completion. A standard approach is to assign zero-mean Gaussian priors on the columns or rows of fac…
Matrix CompletionMatrix Factorization / DecompositionText Mining using Nonnegative Matrix Factorization and Latent Semantic Analysis
Text clustering is arguably one of the most important topics in modern data mining. Nevertheless, text data require tokenization which usually yields a very large and highly sparse term-document matrix, which is usually …
ClusteringMatrix Factorization / DecompositionText ClusteringInstance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning
One way to deal with the ever increasing amount of available data for processing is to rank data instances by usefulness and reduce the dataset size. In this work, we introduce a framework to achieve this using matrix de…
ClusteringDimensionality ReductionMatrix Factorization / DecompositionPrototype Selection+1Eigenvalue and Generalized Eigenvalue Problems: Tutorial
This paper is a tutorial for eigenvalue and generalized eigenvalue problems. We first introduce eigenvalue problem, eigen-decomposition (spectral decomposition), and generalized eigenvalue problem. Then, we mention the o…
BIG-bench Machine LearningMatrix Factorization / DecompositionJoint Matrix-Tensor Factorization for Knowledge Base Inference
While several matrix factorization (MF) and tensor factorization (TF) models have been proposed for knowledge base (KB) inference, they have rarely been compared across various datasets. Is there a single model that perf…
Knowledge Base CompletionKnowledge Base PopulationKnowledge Graph CompletionKnowledge Graph Embedding+1Identification of refugee influx patterns in Greece via model-theoretic analysis of daily arrivals
The refugee crisis is perhaps the single most challenging problem for Europe today. Hundreds of thousands of people have already traveled across dangerous sea passages from Turkish shores to Greek islands, resulting in t…
Dictionary LearningMatrix Factorization / Decomposition