論文筆記：All-at-once Optimization for Coupled Matrix and Tensor Factorizations
一、本文創新點 二、建模 三、CMTF-OPT 四、處理缺失數據的耦合張量-矩陣分解 一、本文創新點 1、大家都清楚，如果有多組數據進行分析，缺失的數據的自由度自然就低了，也更方便進行預測或天從，因此引起了大家對耦合張量，耦合矩陣研究的熱潮。本文只要針對張量與矩陣耦合的問題（簡稱CMTF),提出了一種基於梯度優化的算法（稱爲CMTF-OPT） 2、CMTF-OPT的提出是針對已存在的ALS算法
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相關文章
- 1. 論文筆記：Coupled Tensor Completion via Low-rank Tensor Ring
- 2. 【論文筆記】Embedding of Embedding (EOE) : Joint Embedding for Coupled Heterogeneous Networks
- 3. 論文筆記 ：Tensor Factorization for Low-Rank Tensor Completion
- 4. 論文筆記_2010-BMVC-Joint optimization for object class segmentation and dense stereo reconstruction
- 5. 【論文筆記】Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detecti
- 6. 論文學習筆記 - Classifification of Hyperspectral and LiDAR Data Using Coupled CNNs
- 7. 論文筆記：Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
- 8. 【論文筆記】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
- 9. 2.5 Matrix factorizations (矩陣因式分解)
- 10. 論文筆記：Graph Convolutional Matrix Completion