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Multi-modal data are prevalent in many scientific fields. In this study, we consider the parameter estimation and variable selection for a multi-response regression using block-missing multi-modal data. Our method allows the dimensions of both the responses and the predictors to be large, and the responses to be incomplete and correlated, a common practical problem in high-dimensional settings. Our proposed method uses two steps to make a prediction from a multi-response linear regression model with block-missing multi-modal predictors. In the first step, without imputing missing data, we use all available data to estimate the covariance matrix of the predictors and the cross-covariance matrix between the predictors and the responses. In the second step, we use these matrices and a penalized method to simultaneously estimate the precision matrix of the response vector, given the predictors, and the sparse regression parameter matrix. Lastly, we demonstrate the effectiveness of the proposed method using theoretical studies, simulated examples, and an analysis of a multi-modal imaging data set from the Alzheimer’s Disease Neuroimaging Initiative.
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Fast Multi-Modal Multi-Instance Support Vector Machine for Fine-grained Chest X-ray Recognition
Chest X-ray (CXR) analysis plays an important role in patient treatment. As such, a multitude of machine learning models have been applied to CXR datasets attempting automated analysis. However, each patient has a differing number of images per angle, and multi-modal learning should deal with the missing data for specific angles and times. Furthermore, the large dimensionality of multi-modal imaging data with the shapes inconsistent across the dataset introduces the challenges in training. In light of these issues, we propose the Fast Multi-Modal Support Vector Machine (FMMSVM) which incorporates modality-specific factorization to deal with missing CXRs in the specific angle. Our model is able to adjust the fine-grained details in feature extraction and we provide an efficient optimization algorithm scalable to a large number of features. In our experiments, FMMSVM shows clearly improved classification performance.
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- PAR ID:
- 10508017
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE ICDM 2023
- ISBN:
- 979-8-3503-0788-7
- Page Range / eLocation ID:
- 1295 to 1300
- Subject(s) / Keyword(s):
- Scalability, Multi-Instance, Multi-Modal, Support Vector Machine
- Format(s):
- Medium: X
- Location:
- Shanghai, China
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/ICDM58522.2023.00164
