Dimensionality Reduction with Subspace Structure Preservation
Authors: Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our theoretical analysis with empirical results on both synthetic and real world data achieving state-of-the-art results compared to popular dimensionality reduction techniques. |
| Researcher Affiliation | Academia | Devansh Arpit Department of Computer Science SUNY Buffalo Buffalo, NY 14260 devansha@buffalo.edu Ifeoma Nwogu Department of Computer Science SUNY Buffalo Buffalo, NY 14260 inwogu@buffalo.edu Venu Govindaraju Department of Computer Science SUNY Buffalo Buffalo, NY 14260 govind@buffalo.edu |
| Pseudocode | Yes | Algorithm 1 Computation of projection matrix P INPUT: X,K,λ, itermax |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | For real world data, we use the following datasets: 1. Extended Yale dataset B [3]: It consists of 2414 frontal face images of 38 individuals (K = 38) with 64 images per person. [...] 2. AR dataset [10]: This dataset consists of more than 4000 frontal face images of 126 individuals with 26 images per person. [...] 3. PIE dataset [12]: The pose, illumination, and expression (PIE) database is a subset of CMU PIE dataset consisting of 11, 554 images of 68 people (K = 68). |
| Dataset Splits | Yes | For Extended Yale dataset B, we use all 38 classes for evaluation with 50% 50% train-test split 1 and 70% 30% train-test split 2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, or programming languages with versions). |
| Experiment Setup | Yes | Algorithm 1 lists INPUT: X,K,λ, itermax. Lambda (λ) and itermax are specific hyperparameters for the algorithm. Section 3.3 states: 'Assume that we run this while loop for T iterations and that we use conjugate gradient descent to solve the quadratic program in each iteration.' |