Multimodal Structure Preservation Learning
Chang Liu, Jieshi Chen, Lee H. Harrison, Artur Dubrawski
TL;DR
This paper introduces Multimodal Structure Preservation Learning (MSPL), a novel approach that leverages structural information from one data modality to improve the representation learning of another, enhancing analysis in diverse applications.
Contribution
MSPL is a new method that uses clustering structure from one modality to enhance data representations in another, demonstrating improved structure recovery across synthetic and real-world datasets.
Findings
MSPL effectively uncovers latent structures in synthetic time series data.
MSPL successfully recovers clusters in genome sequencing and antimicrobial resistance data.
MSPL enhances feature utility by embedding external structural information.
Abstract
When selecting data to build machine learning models in practical applications, factors such as availability, acquisition cost, and discriminatory power are crucial considerations. Different data modalities often capture unique aspects of the underlying phenomenon, making their utilities complementary. On the other hand, some sources of data host structural information that is key to their value. Hence, the utility of one data type can sometimes be enhanced by matching the structure of another. We propose Multimodal Structure Preservation Learning (MSPL) as a novel method of learning data representations that leverages the clustering structure provided by one data modality to enhance the utility of data from another modality. We demonstrate the effectiveness of MSPL in uncovering latent structures in synthetic time series data and recovering clusters from whole genome sequencing and…
Peer Reviews
Decision·Submitted to ICLR 2025
I think the paper has several strenghs: 1: It presents a flexible framework that can incorporate different modality as inputs, incorporating various loss functions and clustering objectives 2: It addresses a real-world problem in epidemiology (using MALDI data as a cost-effective alternative to WGS) 3: It introduces a new cluster evaluation metric (cluster F1 score)
This paper has several areas that can be improved: 1: lt could benefit from more extensive comparison with other multimodal learning approaches 2: Authors could explore more sophisticated structure preservation objectives The three losses are common objective functions in multimodal and VE/VAE variants. Besides, there is limited discussion of the impact of different encoder architectures 3: Model needs further optimization. Even comparing with its own variants, the proposed model cannot outperfo
The concept of preserving structure level alignment without need for the entire dataset is interesting, and the proposed approach appears to be novel. The application of multimodal deep representation learning approaches of this kind to mass spectrometry data in the context of epidemiology is particularly original and exciting. The method is very clearly described, as is the evaluation approach and the metrics used. In evaluating, the authors considered extrinsic clustering metrics that went b
The paper has several significant weaknesses. First, the significance of the method’s real-world impact in the application area is somewhat unclear. The introduction states that the main utility of the learned representation in this context is that it could replace WGS in practice as a more cost-effective alternative; however, the method seems to require SNP distances between each pair of samples (and thus WGS for every sample) as an input in order to learn the representation. As such, it is n
The stated problem is pervasive in biomedical applications and is challenging.
1) This is a typical subset of domain adaptation problems. However, they did not include SOTA domain adaptation methods into the baseline. The baseline methods are weak. 2) Also, from references we see that there are already methods that perform prediction tasks directly based on MALDI, which were not compared. 3) The experiments are carried out only on MALDI-WGS datasets and most are synthetic datasets. Due to the small-sample nature of these problems, the models are vulnerable to short-cut lea
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Taxonomy
TopicsNatural Language Processing Techniques · Speech and dialogue systems · Text and Document Classification Technologies