Abstract:
Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providing a set of tools from algebra, topology, and geometry to extract features...Show MoreMetadata
Abstract:
Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providing a set of tools from algebra, topology, and geometry to extract features from data based on its topological and geometrical features. This paper combines available methods from topological data analysis including persistent homology, persistent entropy, and persistent diagrams to build a strong topological feature extractor model from the topological properties of an image. By feeding features extracted by the topological model to machine learning models, we perform the classification task on DeepSat (SAT-4) dataset.
Date of Conference: 15-17 December 2020
Date Added to IEEE Xplore: 10 February 2021
ISBN Information:
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- IEEE Keywords
- Index Terms
- Topological Analysis ,
- Topological Data Analysis ,
- Machine Learning ,
- Field Of Science ,
- Algebra ,
- Topological Features ,
- High-dimensional ,
- Learning Algorithms ,
- Convolutional Neural Network ,
- Random Forest ,
- Dimensionality Reduction ,
- Spinal Cord Injury ,
- Morphological Operations ,
- Deep Belief Network ,
- Simplicial Complex ,
- Faster Runtime ,
- Dimensional Complex ,
- Erosion Operation
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Topological Analysis ,
- Topological Data Analysis ,
- Machine Learning ,
- Field Of Science ,
- Algebra ,
- Topological Features ,
- High-dimensional ,
- Learning Algorithms ,
- Convolutional Neural Network ,
- Random Forest ,
- Dimensionality Reduction ,
- Spinal Cord Injury ,
- Morphological Operations ,
- Deep Belief Network ,
- Simplicial Complex ,
- Faster Runtime ,
- Dimensional Complex ,
- Erosion Operation
- Author Keywords