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Found 6 papers, 5 papers with code
DCSI -- An improved measure of cluster separability based on separation and connectedness
Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.
Enhancing cluster analysis via topological manifold learning
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: theoretical arguments and empirical evidence show that clustering embedding vectors, representing the structure of a data manifold instead of the observed feature vectors themselves, is highly beneficial.
A geometric framework for outlier detection in high-dimensional data
Outlier or anomaly detection is an important task in data analysis.
A geometric perspective on functional outlier detection
We consider functional outlier detection from a geometric perspective, specifically: for functional data sets drawn from a functional manifold which is defined by the data's modes of variation in amplitude and phase.
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction
The contributions of the paper are three-fold: First of all, we define a theoretical framework which allows to systematically assess specific challenges that arise in the functional data context, transfer several nonlinear dimension reduction methods for tabular and image data to functional data, and show that manifold methods can be used successfully in this setting.
Large-scale benchmark study of survival prediction methods using multi-omics data
The Kaplan-Meier estimate and a Cox model using only clinical variables were used as reference methods.
