Enhancing Sufficient Dimension Reduction via Hellinger Correlation
Authors: Seungbeom Hong, Ilmun Kim, Jun Song
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive numerical experiments, we demonstrate that our proposed method significantly enhances and outperforms existing SDR methods. In Section 4, we provide simulation results that compare our method with existing ones. Section 5 presents a real data application of the method. |
| Researcher Affiliation | Academia | 1Department of Statistics, Korea University, Seoul, South Korea 2Department of Applied Statistics, Yonsei University, Seoul, South Korea. |
| Pseudocode | No | The paper describes the method in prose but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code that implements our proposed method is available at https://github.com/JSong Lab/SDR HC. |
| Open Datasets | Yes | We apply our methods to the real estate valuation dataset in the UCI Machine Learning Repository (Yeh, 2018). |
| Dataset Splits | No | Subsequently, we randomly divide the dataset into a training sample of size 300 and use the remaining objects as the test sample. (No explicit mention of a validation split.) |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch x.x). |
| Experiment Setup | Yes | To assess the performance of our method, we employ the following metric to quantify the difference between two subspaces: (ST rue, SEstimated) = PST rue PSEstimated , where is the maximum eigenvalue of a matrix and PST rue and PSEstimated are the orthogonal projection matrices of the subspace ST rue = Span(η ) and SEstimated = Span(ˆη). A smaller value of indicates a more accurate estimation. Subsequently, we randomly divide the dataset into a training sample of size 300 and use the remaining objects as the test sample. SDR methods are then applied to the training set to extract the SDR direction, followed by fitting a local polynomial regression using the remaining variables to predict the house price. The weights are given equally for each observation and quadratic polynomial was used to fit the model. |