Single Pass PCA of Matrix Products
Authors: Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | in addition we also provide results from an Apache Spark implementation1 that shows better computational and statistical performance on real-world and synthetic evaluation datasets. |
| Researcher Affiliation | Academia | Shanshan Wu The University of Texas at Austin shanshan@utexas.edu Srinadh Bhojanapalli Toyota Technological Institute at Chicago srinadh@ttic.edu Sujay Sanghavi The University of Texas at Austin sanghavi@mail.utexas.edu Alexandros G. Dimakis The University of Texas at Austin dimakis@austin.utexas.edu |
| Pseudocode | Yes | Algorithm 1 SMP-PCA: Streaming Matrix Product PCA |
| Open Source Code | Yes | The source code is available at [18].S. Wu, S. Bhojanapalli, S. Sanghavi, and A. Dimakis. Github repository for "single-pass pca of matrix products". https://github.com/wushanshan/Matrix Product PCA, 2016. |
| Open Datasets | Yes | We test our algorithm on synthetic datasets and three real datasets: SIFT10K [9], NIPS-BW [11], and URL-reputation [12]. |
| Dataset Splits | No | The paper mentions using specific datasets (SIFT10K, NIPS-BW, URL-reputation) but does not provide explicit details on how these datasets were split into training, validation, or test sets, nor does it specify proportions or sample counts for each split. |
| Hardware Specification | Yes | using a 150GB synthetic dataset on m3.2xlarge Amazon EC2 instances6. ... 6Each machine has 8 cores, 30GB memory, and 2 80GB SSD. |
| Software Dependencies | Yes | We implement our SMP-PCA in Apache Spark 1.6.2 [19]. |
| Experiment Setup | Yes | For all rest experiments, unless otherwise speciļ¬ed, we set r = 5, T = 10, and m as 4nr log n. |