Topological Data Analysis
125 papers with code • 0 benchmarks • 3 datasets
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Latest papers with no code
PHLP: Sole Persistent Homology for Link Prediction -- Interpretable Feature Extraction
Link prediction (LP), inferring the connectivity between nodes, is a significant research area in graph data, where a link represents essential information on relationships between nodes.
Topological Feature Search Method for Multichannel EEG: Application in ADHD classification
Then, multi-dimensional time series are re-embedded, and TDA is applied to obtain topological feature information.
The Shape of Word Embeddings: Recognizing Language Phylogenies through Topological Data Analysis
Word embeddings represent language vocabularies as clouds of $d$-dimensional points.
Learning Topological Representations for Deep Image Understanding
In many scenarios, especially biomedical applications, the correct delineation of complex fine-scaled structures such as neurons, tissues, and vessels is critical for downstream analysis.
Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ
In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, so as to capture semantic information and determine the necessity for re-transmission.
Quantum Distance Approximation for Persistence Diagrams
Topological Data Analysis methods can be useful for classification and clustering tasks in many different fields as they can provide two dimensional persistence diagrams that summarize important information about the shape of potentially complex and high dimensional data sets.
Diversity-Aware Ensembling of Language Models Based on Topological Data Analysis
Ensembles are important tools for improving the performance of machine learning models.
Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning
Our analysis of these two failure cases of DNNs reveals that finding a unified solution for shortcut learning in DNNs is not out of reach, and TDA can play a significant role in forming such a framework.
WGTDA: A Topological Perspective to Biomarker Discovery in Gene Expression Data
Advancing the discovery of prognostic cancer biomarkers is crucial for comprehending disease mechanisms, refining treatment plans, and improving patient outcomes.
Novel definition and quantitative analysis of branch structure with topological data analysis
Our method can be applied to a wide range of branch structures in biology, enabling objective analysis of numbers, spatial distributions, sizes, and more.
