An Efficient Approach to Informative Feature Extraction from Multimodal Data
Authors: Lichen Wang, Jiaxiang Wu, Shao-Lun Huang, Lizhong Zheng, Xiangxiang Xu, Lin Zhang, Junzhou Huang5281-5288
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize. In this section, we evaluate Soft-HGR in the following aspects: To verify the relationship between the HGR features and Soft-HGR feature is linear; To compare the efficiency and numerical stability of CCA based models and Soft-HGR; To demonstrate the power of semi-supervised Soft-HGR on discriminative tasks with limited labels; To show the performance of Soft-HGR on more than two modalities and missing modalities. |
| Researcher Affiliation | Collaboration | Lichen Wang,1 Jiaxiang Wu,2 Shao-Lun Huang,1 Lizhong Zheng,3 Xiangxiang Xu,4 Lin Zhang,1 Junzhou Huang5 1Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, 2Tencent AI Lab 3Department of EECS, Massachusetts Institute of Technology 4Department of Electronic Engineering, Tsinghua University 5Department of CSE, The University of Texas at Arlington |
| Pseudocode | Yes | Algorithm 1 Evaluate Soft-HGR on a mini-batch |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MNIST handwritten image dataset (Le Cun et al. 1998), which consists of 60K/10K gray-scale digit images of size 28 28 as training/testing sets. University of Wisconsin X-ray Microbeam Database (XRMB) (Westbury 1994). KKBox s Music Recommendation Dataset (Chen et al. 2018). |
| Dataset Splits | Yes | The hyper-parameters for each model are determined by their best average performance on validation set on 5-fold cross validation. we use the last 20% of 7M training data as test set. |
| Hardware Specification | Yes | Both optimizations are executed on a Nvidia Tesla K80 GPU with mini-batch SGD of 5K batchsize. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries, such as Python versions or deep learning framework versions. |
| Experiment Setup | Yes | Both optimizations are executed on a Nvidia Tesla K80 GPU with mini-batch SGD of 5K batchsize. The DNN structure for each neural branches may vary, depending on the statistical property of the inputs. The output feature dimensions k is chosen to be 80 for all methods. The hyper-parameters for each model are determined by their best average performance on validation set on 5-fold cross validation. batch normalization (Ioffe and Szegedy 2015) is applied before Re LU activation function to ensure better convergence. |