Compact Random Feature Maps
Authors: Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on multiple standard data-sets with performance competitive with state-of-the-art results. We now present CRAFTMaps results on multiple data-sets. |
| Researcher Affiliation | Collaboration | Raffay Hamid RAFFAY@CC.GATECH.EDU e Bay Research Laboratory Ying Xiao YING.XIAO@GATECH.EDU Georgia Institute of Technology Alex Gittens AGITTENS@EBAY.COM e Bay Research Laboratory Dennis De Coste DDECOSTE@EBAY.COM e Bay Research Laboratory |
| Pseudocode | Yes | Algorithm 1 RANDOM FEATURE MAPS (RFM) Algorithm 2 CRAFTMAPS USING RFM |
| Open Source Code | No | The paper does not contain any statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | Figure 5 shows the normalized root mean square errors (NRMSE) for MNIST data obtained for an r = 7 and q = 1 kernel using random feature maps... Figure 6 where NRMSE for 6 different data-sets over a range of E are shown. Table 1 shows the test classification error using CRAFTMaps on random feature maps (Kar & Karnick, 2012) and tensor sketch (Pham & Pagh, 2013) compared to 5 alternate approaches over 4 different data-sets. (These datasets are MNIST, USPS, COIL100, PENDIGITS) Results on MNIST8M Data Figure 7 (b) shows the comparative performance of CRAFTMaps for a given sized E (2^14) as training size varies from 60K to 8.1M. |
| Dataset Splits | No | The paper mentions 'training data' and 'test points' and uses 'randomly selected' or 'randomly picked' points, but it does not specify explicit train/validation/test splits, percentages, or absolute counts for validation data. |
| Hardware Specification | No | The paper mentions 'These times were recorded for MNIST data using a 40-core machine.' This provides a general core count but lacks specific CPU models, GPU details, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions 'BLAS3' in relation to exploiting multi-core processing power, but it does not list any specific software libraries or their version numbers. |
| Experiment Setup | Yes | Unless otherwise mentioned, we use the H-0/1 heuristic of (Kar & Karnick, 2012) for random feature maps (RFM) and CRAFTMaps on RFM. These results were obtained for an r = 7 and q = 1 kernel. Here r = 7, 7, 5, 5 and 9 respectively while q = 1. These results were obtained using a polynomial kernel with r = 7, q = 1, D = 2^17, E = 2^14, and ECOCs equal to 200. |