Orthogonal NMF through Subspace Exploration
Authors: Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithms on several real and synthetic datasets and show that their performance matches or outperforms the state of the art. |
| Researcher Affiliation | Academia | Megasthenis Asteris The University of Texas at Austin megas@utexas.eduDimitris Papailiopoulos University of California, Berkeley dimitrisp@berkeley.eduAlexandros G. Dimakis The University of Texas at Austin dimakis@austin.utexas.edu |
| Pseudocode | Yes | Algorithm 1 Low Rank NNPCA Algorithm 2 ONMFS Algorithm 3 Local Opt W |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | CBCL Dataset. The CBCL dataset [30] contains 2429, 19 19 pixel, gray scale face images. It has been used in the evaluation of all three methods [16, 17, 27]. Additional Datasets. We solve the NNPCA problem on various datasets obtained from [31]. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits for training, validation, or testing, nor does it mention cross-validation setups. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions general tools like SVD, but it does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | For our algorithm, we use a sketch of rank r = 4 of the (centered) input data. Further we apply an early termination criterion; execution is terminated if no improvement is observed in a number of consecutive iterations (samples). We set a high penalty (α = 1e10) to promote orthogonality. We run ONMF methods with target dimension k = 5. For the methods that involved random initialization, we run 10 averaging iterations per Monte Carlo trial. We compare our algorithm with several state-of-the-art ONMF algorithms i) the O-PNMF algorithm of [13] (for 1000 iterations), and ii) the more recent ONP-MF iii) EM-ONMF algorithms of [11, 32] (for 1000 iterations). |