Researchers at Purdue University have developed a novel approach, graph-based topological data analysis (GTDA), to simplify the interpretation of complex predictive models such as deep neural networks. These models often pose challenges in understanding and generalization. GTDA uses topological data analysis to transform complex prediction landscapes into simplified topological maps.
Unlike traditional methods such as tSNE and UMAP, GTDA provides more specific inspection of model results. The method involves the construction of a Reeb network, a discretization of topological structures, to simplify the data while respecting the topology. Based on the mapper algorithm, this recursive split-merge procedure constructs a discrete approximation of the Reeb graph. GTDA starts with a graph that represents the relationships between data points and uses lenses, such as neural network prediction matrices, to guide the analysis. The recursive splitting strategy helps to build containers in the multidimensional space.
GTDA uses a transformer-based model, Enformer, designed to predict gene expression levels based on DNA sequences. Analysis of harmful mutations in the BRCA1 gene demonstrated the ability of GTDA to highlight biologically relevant characteristics. GTDA showed the localization of predictions in the DNA sequence and provided information on the impact of mutations in specific genetic regions.
The GTDA framework also offers automatic error estimation, overcoming model uncertainty in certain cases. Analysis of a chest x-ray dataset revealed incorrect diagnostic annotations, emphasizing the potential of GTDA to identify errors in deep learning datasets. The method was further applied to a ResNet50 model pre-trained on the Imagenette dataset, providing a visual taxonomy of images and discovering mislabeled data points. The scalability of GTDA was demonstrated by analyzing over a million images on ImageNet, in approximately 7.24 hours.
The researchers compared GTDA with traditional methods such as tSNE and UMAP on different data sets, demonstrating the effectiveness of GTDA in providing detailed information. The method was also applied to study chest X-ray diagnosis and compare deep learning frameworks, demonstrating its versatility. GTDA offers a promising solution to the challenges of interpreting complex predictive models. Its ability to simplify topological landscapes provides detailed information on predictive mechanisms and facilitates the identification of biologically relevant features. The scalability and applicability of the method to diverse data sets make it a valuable tool for understanding and improving prediction models in various domains.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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