Matrix factorization with neural networks
Matrix factorization is an important mathematical problem encountered in the context of dictionary learning, recommendation systems and machine learning. We introduce a new `decimation' scheme that maps it to neural network models of associative memory and provide a detailed theoretical analysis of its performance, showing that decimation is able to factorize extensive-rank matrices and to denoise them efficiently. We introduce a decimation algorithm based on ground-state search of the neural network, which shows performances that match the theoretical prediction.
Code (0)
등록된 구현이 없습니다.
Tasks
Dictionary LearningRecommendation SystemsSimilar Papers 제목 키워드 기반
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior
We derive analytical expression of matrix factorization/completion solution by variational Bayes method, under the assumption that observed matrix is originally the product of low-rank dense and sparse matrices with addi…
Matrix CompletionOnline Matrix Factorization via Broyden Updates
In this paper, we propose an online algorithm to compute matrix factorizations. Proposed algorithm updates the dictionary matrix and associated coefficients using a single observation at each time. The algorithm performs…
MatMat: Matrix Factorization by Matrix Fitting
Matrix factorization is a widely adopted recommender system technique that fits scalar rating values by dot products of user feature vectors and item feature vectors. However, the formulation of matrix factorization as a…
FairnessMulti-Task LearningRecommendation SystemsMulti-Mode Deep Matrix and Tensor Factorization
Recently, deep linear and nonlinear matrix factorizations gain increasing attention in the area of machine learning. Existing deep nonlinear matrix factorization methods can only exploit partial nonlinearity of the data …
Tensor DecompositionRankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement
Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, di…
FairnessRecommendation SystemsWord Embeddings