Multidimensional Scaling on Multiple Input Distance Matrices
Authors: Song Bai, Xiang Bai, Longin Jan Latecki, Qi Tian
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic as well as real datasets demonstrate the effectiveness of MVMDS. |
| Researcher Affiliation | Academia | Song Bai,1 Xiang Bai,1 Longin Jan Latecki,2 Qi Tian3 1Huazhong University of Science and Technology 2Temple University, 3University of Texas at San Antonio {songbai, xbai}@hust.edu.cn, latecki@temple.edu, qitian@cs.utsa.edu |
| Pseudocode | Yes | Algorithm 1: Multi-View Multidimensional Scaling. |
| Open Source Code | No | The paper does not include any explicit statement about providing open-source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Two image benchmark datasets, i.e., Microsoft Research Cambridge Volume 1 (MSRC-v1) (Winn and Jojic 2005), Caltech-101 dataset (Fei-Fei, Fergus, and Perona 2007), are selected for performance comparisons. |
| Dataset Splits | No | The paper describes the datasets used and categories selected (e.g., '30 images per category' for MSRC-v1, '7 classes and 20 classes forming Caltech101-7 and Caltech101-20'), but it does not provide specific details on how these datasets were partitioned into training, validation, and test sets (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools and algorithms used (e.g., K-means, SIFT, HOG, LBP, HSV, GIST) but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | All the visual features are L2 normalized, then Euclidean distance is used to measure the dissimilarity between images. All the comparisons are done by using MDS or the proposed MVMDS to project images into P = 10 dimensional space. Since the weight controller γ needs to be determined empirically, we conduct an exhaustive search in the interval (1, 10] with step size 0.5 to find its optimal value. The desired number of clusters is set to be equal to the natural number of categories in each dataset. |