Nonnegative Sparse PCA with Provable Guarantees
Authors: Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros Dimakis
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our scheme on several data sets, showing that it matches or outperforms the previous state of the art. and 7. Experimental Evaluation We empirically evaluate the performance of our algorithm on various datasets and compare it to the EM algorithm |
| Researcher Affiliation | Academia | Megasthenis Asteris MEGAS@UTEXAS.EDU Dimitris S. Papailiopoulos DIMITRIS@UTEXAS.EDU Alexandros G. Dimakis DIMAKIS@AUSTIN.UTEXAS.EDU Department of Electrical and Computer Engineering, The University of Texas at Austin, TX, USA |
| Pseudocode | Yes | Algorithm 1 Spannogram Nonnegative Sparse PCA |
| Open Source Code | No | Matlab implementation available by the author. - This statement is not concrete access to a repository or an explicit release statement. |
| Open Datasets | Yes | CBCL Face Dataset (Sung, 1996), Leukemia Dataset (Armstrong et al., 2001), and Low Resolution Spectrometer (LRS) dataset, available in (Bache & Lichman, 2013) |
| Dataset Splits | No | The paper references datasets but does not provide specific details on training, validation, or test splits, such as percentages, sample counts, or explicit splitting methodologies. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Matlab implementation' but does not specify any software names with version numbers for dependencies. |
| Experiment Setup | Yes | Alg. 3 for d = 3 and ϵ = 0.1, and the EM algorithm exhibit nearly identical performance. and an appropriate sparsity penalty β was determined via binary search for each target sparsity k. |