Morphism-Based Learning for Structured Data
Authors: Kilho Shin, Dave Shepard5767-5775
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a generic and theoretic framework to investigate similarity of structured data through structure-preserving one-to-one partial mappings, which we call morphisms.Although the effectiveness of our framework has been already proven through some experiments, we will run experiments in a larger scale with a wider variation of machine learning methods including but not limited to distance, multiple alignment, pattern extraction and kernel. |
| Researcher Affiliation | Academia | Kilho Shin,1 David Lawrence Shepard2 1Gakushuin University, Japan, 2UCLA Scholarly Innovation Lab., USA |
| Pseudocode | Yes | Abstract Center Star Algorithm Input: X1, . . . , Xn D. Output: μij MXi,Xj for distinct i, j {1, . . . , n} with μij = μ 1 ji and μij μkj μik. Procedures: 1. For each Xi, compute morphisms μij MXi,Xj for j [1,ˆi, n] with d M ϕ,c(Xi, Xj) = Ψϕ,c( μij) and let Si = j [1,ˆi,n] d M ϕ,c(Xi, Xj). 2. Pick k arg min{Si | i = 1, . . . , n}; 3. Determine μki = μki, μik = μ 1 ki and μij = μkj μ 1 ki for i = k and j = k. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It states: 'For this purpose, we have a plan to develop utility programs that analyze an input dataset exhaustively and consistently by means of the morphism distance, the morphism-based pattern extraction and the moment kernels and others derived from appropriately parameterized pairs of (M, ϕ).' |
| Open Datasets | No | The paper is theoretical and focuses on developing a framework; it does not describe or use specific datasets for empirical evaluation within its scope. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets; therefore, no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments; therefore, no specific software dependencies are provided. |
| Experiment Setup | No | The paper is theoretical and focuses on developing a framework; it does not describe experimental setups, hyperparameters, or training settings for empirical evaluation. |