Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
Authors: Jicong Fan, Yuqian Zhang, Madeleine Udell3842-3849
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative studies on synthetic data, subspace clustering with missing data, motion capture data recovery, and transductive learning verify the superiority of our methods over the state-of-the-art. and 7 Experiments We compare the proposed PMC methods with LRMC method (nulcear norm minimization), SRMC (Fan and Chow 2017), LADMC (Ongie et al. 2018) (the iterative algorithm), VMC-2 (2-order polynomial kernel), VMC-3 (3order polynomial kernel)(Ongie et al. 2017), and NLMC |
| Researcher Affiliation | Academia | Cornell University, Ithaca, NY 14853, USA {jf577, yz2557, udell}@cornell.edu |
| Pseudocode | Yes | Algorithm 1 Optimization for PMC using Adam+ Input: X, Ω, k( , ), s or w, γ = 10 6, λ = 10 4, ε = 10 6, tmax, β1 = 0.9, β2 = 0.999, ϵ = 10 8, t = 0 1: initialize [ ˆ X] Ω = 0, ˆx = vec([ ˆ X] Ω), m0 = υ0 = 0 2: repeat 3: t t + 1 4: perform SVD: K( ˆ X) = V SV T 5: compute Θt 1 and ˆ XL( ˆ X, Θt 1) 6: gt vec [ ˆ XL( ˆ X, Θt 1)] Ω 7: mt β1mt 1 + (1 β1)gt 8: υt β2υt 1 + (1 β2)g2 t 9: ˆmt m/(1 βt 1); ˆυt υ/(1 βt 2) 10: ˆxt ˆxt 1 λ ˆmt/( ˆυt + ϵ); [ ˆ X] Ω ˆxt 11: if Lt > Lt 1 12: λ 0.8λ else λ 1.1λ 13: endif 14: until |ˆxt ˆxt 1| < ε or t = tmax Output: ˆ X |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology was found. |
| Open Datasets | Yes | Similar to (Yang, Robinson, and Vidal 2015; Ongie et al. 2017), we perform subspace clustering with missing data on the Hopkins 155 dataset (Tron and Vidal 2007). and We use the trials #1 and #6 of subject #56 of the CMU Mocap dataset. |
| Dataset Splits | No | The proportion of training (labeled) data is 50%. No explicit description of a dedicated validation set or a complete train/validation/test split for all experiments was found. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments) used for running experiments were provided. |
| Software Dependencies | No | The paper mentions using the 'Adam algorithm' but does not specify any software libraries, frameworks, or their version numbers used for implementation (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Algorithm 1 Optimization for PMC using Adam+ Input: X, Ω, k( , ), s or w, γ = 10 6, λ = 10 4, ε = 10 6, tmax, β1 = 0.9, β2 = 0.999, ϵ = 10 8, t = 0 |