Search Results for author: Fabian Scheipl

Found 11 papers, 9 papers with code

DCSI -- An improved measure of cluster separability based on separation and connectedness

1 code implementation19 Oct 2023 Jana Gauss, Fabian Scheipl, Moritz Herrmann

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.

Clustering

Enhancing cluster analysis via topological manifold learning

1 code implementation1 Jul 2022 Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger

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.

Clustering

A geometric perspective on functional outlier detection

1 code implementation14 Sep 2021 Moritz Herrmann, Fabian Scheipl

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.

Outlier Detection

Developing Open Source Educational Resources for Machine Learning and Data Science

no code implementations28 Jul 2021 Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl

It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS).

BIG-bench Machine Learning

Multivariate Functional Additive Mixed Models

1 code implementation11 Mar 2021 Alexander Volkmann, Almond Stöcker, Fabian Scheipl, Sonja Greven

Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary like precipitation, temperature, and wind speeds over time at a given weather station.

Methodology

Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction

1 code implementation22 Dec 2020 Moritz Herrmann, Fabian Scheipl

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.

Dimensionality Reduction

A General Machine Learning Framework for Survival Analysis

1 code implementation27 Jun 2020 Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl

The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events.

BIG-bench Machine Learning Data Augmentation +1

Benchmarking time series classification -- Functional data vs machine learning approaches

1 code implementation18 Nov 2019 Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl

In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.

Additive models Benchmarking +6

pammtools: Piece-wise exponential Additive Mixed Modeling tools

2 code implementations4 Jun 2018 Andreas Bender, Fabian Scheipl

This article introduces the pammtools package, which facilitates data transformation, estimation and interpretation of Piece-wise exponential Additive Mixed Models.

Computation

Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models

1 code implementation26 May 2011 Fabian Scheipl, Ludwig Fahrmeir, Thomas Kneib

Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms.

Methodology Applications

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