Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach
Authors: Wenye Li
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carried out a series of empirical experiments and the results confirmed our theoretical justification. The evaluation also reported significantly improved results in real learning tasks on benchmark datasets. |
| Researcher Affiliation | Academia | Wenye Li Macao Polytechnic Institute Macao SAR, China wyli@ipm.edu.mo |
| Pseudocode | Yes | Algorithm 1 Projection onto R = S T |
| Open Source Code | No | The paper does not provide a direct link or explicit statement for the open-sourcing of the code for its proposed methodology. |
| Open Datasets | Yes | To evaluate the performance of the proposed method, four benchmark datasets were used in our experiments. MNIST: a grayscale image database of handwritten digits ( 0 to 9 )... USPS: another grayscale image database of handwritten digits... PROTEIN: a bioinformatics database... WEBSPAM: a dataset with both spam and non-spam web pages. |
| Dataset Splits | No | The paper states 'In each run, 90% of the samples were randomly chosen as the training set and the remaining 10% were used as the testing set.' It does not explicitly mention a separate validation set for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper mentions 'on our platform' when discussing computational time but does not provide specific hardware details such as exact CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | Yes | For each dataset, we experimented with 1, 000 and 10, 000 samples respectively. For each sample, different portions (from 10% to 90%) of feature values were marked as missing... For the k NN approach, we iterated different k from 1 to 5 and the best result was collected... In each run, 90% of the samples were randomly chosen as the training set and the remaining 10% were used as the testing set. The mean and standard deviation of the classification errors in 1, 000 runs were reported. |