Recovery of Coherent Data via Low-Rank Dictionary Pursuit
Authors: Guangcan Liu, Ping Li
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on randomly generated matrices and real motion sequences verify our claims. |
| Researcher Affiliation | Academia | Guangcan Liu Department of Statistics and Biostatistics Department of Computer Science Rutgers University Piscataway, NJ 08854, USA gcliu@rutgers.edu Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University Piscataway, NJ 08854, USA pingli@rutgers.edu |
| Pseudocode | Yes | Algorithm 1 Matrix Recovery input: Observed data matrix X Rm n. adjustable parameter: λ. 1. Solve for ˆL0 by optimizing the RPCA problem (1.2) with λ = 1/ n1. 2. Estimate the rank of ˆL0 by ˆr0 = #{i : σi > 10 3σ1}, where σ1, σ2, , σn2 are the singular values of ˆL0. 3. Form L0 by using the rank-ˆr0 approximation of ˆL0. That is, L0 = arg min L L ˆL0 2 F, s.t. rank(L) ˆr0, which is solved by SVD. 4. Construct a dictionary ˆA from L0 by normalizing the column vectors of L0: [ ˆA]:,i = [ L0]:,i [ L0]:,i 2 , i = 1, , n, where [ ]:,i denotes the ith column of a matrix. 5. Solve for Z by optimizing the LRR problem (1.3) with A = ˆA and λ = 1/ n1. output: ˆAZ . |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing its code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We now present our experiment with 11 additional sequences attached to the Hopkins155 [21] database. |
| Dataset Splits | No | The paper describes generating random matrices and using corrupted motion sequences from Hopkins155, but does not provide specific train/validation/test split percentages, sample counts, or references to predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions methods like RPCA and LRR, but does not provide specific software dependencies or version numbers for any libraries or tools used in the implementation. |
| Experiment Setup | Yes | In the experiments of this paper, we consistently set ε = 10 6 X F. and We replace each missed entry with a number from Bernoulli 1 and both using λ = 1/ n1. |