Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
Authors: Jonas W. Mueller, Tommi Jaakkola
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 ExperimentsFigure 1a illustrates the cost function of PDA pertaining to two 3-dimensional distributions (see details in Supplementary Information S1). The synthetic MADELON dataset used in the NIPS 2003 feature selection challenge consists of points (n m 1000, d 500)...Figure 1b demonstrates how well SPARDA (red), the top sparse principal component (black) [27], sparse LDA (green) [2], and the logistic lasso (blue) [12] are able to identify the 20 relevant features over different settings of their respective regularization parameters... |
| Researcher Affiliation | Academia | Jonas Mueller CSAIL, MIT jonasmueller@csail.mit.edu Tommi Jaakkola CSAIL, MIT tommi@csail.mit.edu |
| Pseudocode | Yes | RELAX Algorithm: Solves the dualized semidefinite relaxation of SPARDA (7). Returns the largest eigenvector of the solution to (6) as the desired projection direction for SPARDA. and Projection Algorithm: Projects matrix onto positive semidefinite cone of unit-trace matrices Br (the feasible set in our relaxation). |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | Yes | The synthetic MADELON dataset used in the NIPS 2003 feature selection challenge consists of points (n m 1000, d 500)... and We apply SPARDA to expression measurements of 10,305 genes profiled in 1,691 single cells from the somatosensory cortex and 1,314 hippocampus cells sampled from the brains of juvenile mice [29]. |
| Dataset Splits | No | The paper mentions 'cross-validation' for parameter selection but does not provide specific details on train/validation/test splits (e.g., percentages, sample counts, or predefined split citations). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions regularization parameters but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or training configurations. |