Optimal Sparse Linear Encoders and Sparse PCA
Authors: Malik Magdon-Ismail, Christos Boutsidis
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We answer both questions by providing the first polynomial-time algorithms to construct optimal sparse linear auto-encoders; additionally, we demonstrate the performance of our algorithms on real data. Our experiments are not exhaustive, but their role is modest: to motivate minimizing loss as the right machine learning objective for sparse encoders (Problem 1). We empirically demonstrate our algorithms against existing state-of-the-art sparse PCA methods. |
| Researcher Affiliation | Academia | Malik Magdon-Ismail Rensselaer Polytechnic Institute, Troy, NY 12211 magdon@cs.rpi.edu Christos Boutsidis New York, NY christos.boutsidis@gmail.com |
| Pseudocode | Yes | Blackbox algorithm to compute encoder from CSSP. Batch Sparse Linear Encoder Algorithm. Iterative Sparse Linear Encoder Algorithm. |
| Open Source Code | No | The paper does not provide explicit links or statements about the availability of its own source code for the described methodology. |
| Open Datasets | Yes | We use the same data sets used by these prior algorithms (all available in [23]): Pit Props (X R13 13); Colon (X R500 500); Lymphoma (X R500 500). |
| Dataset Splits | Yes | The table below compares the 10-fold cross-validation error Eout for an SVM classifier using features from popular variance maximizing sparse-PCA encoders and our loss minimizing sparse-encoder (k = 6 and r = 7), |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper discusses various algorithms and methods but does not list specific software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | The table below compares the 10-fold cross-validation error Eout for an SVM classifier using features from popular variance maximizing sparse-PCA encoders and our loss minimizing sparse-encoder (k = 6 and r = 7). The inputs are X Rn d, the number of components k and the sparsity parameter r. We only show k = 2 in Figure 1. |