Binary Embedding: Fundamental Limits and Fast Algorithm
Authors: Xinyang Yi, Constantine Caramanis, Eric Price
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical findings are supported through experiments on both synthetic and real data sets. |
| Researcher Affiliation | Academia | Xinyang Yi YIXY@UTEXAS.EDU Constantine Caramanis CONSTANTINE@UTEXAS.EDU Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712 Eric Price ECPRICE@CS.UTEXAS.EDU Department of Computer Science, The University of Texas at Austin, Austin, TX 78712 |
| Pseudocode | Yes | Algorithm 1 Uniform Random Projection, Algorithm 2 Fast Binary Embedding, and Algorithm 3 Alternative Fast Binary Embedding are all present in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about making its source code open, nor does it provide a link to a code repository for its methodology. |
| Open Datasets | Yes | A popular application of binary embedding is image retrieval, as considered in (Gong & Lazebnik, 2011; Gong et al., 2013; Yu et al., 2014). We thus conduct an experiment on the Flickr-25600 dataset that consists of 10k images from Internet. |
| Dataset Splits | No | The paper describes using 500 randomly sampled images as query points and the rest as a base for retrieval, but it does not specify explicit training, validation, or test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper discusses computational complexity but does not provide any specific details about the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies, such as libraries or solvers, along with their version numbers. |
| Experiment Setup | Yes | Algorithm FBE needs parameters n, B, which are intermediate dimension and number of blocks respectively. Based on Theorem 3.8, n is required to be proportional to m (up to some logarithmic factors) and B is required to be proportional to log N. We thus set n 1.3m, B 1.8 log N. We also set n 1.3m for FBE-2. In addition, we fix p = 512. |