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Found 35 papers, 5 papers with code
What is the $\textit{intrinsic}$ dimension of your binary data? -- and how to compute it quickly
Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data.
A Repository for Formal Contexts
However, the distribution of the data sets poses a problem for the sustainable development of the research field.
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years.
The Geometric Structure of Topic Models
We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.
Towards Ordinal Data Science
One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations.
Automatic Textual Explanations of Concept Lattices
Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales.
Ordinal Motifs in Lattices
Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge.
Selecting Features by their Resilience to the Curse of Dimensionality
Real-world datasets are often of high dimension and effected by the curse of dimensionality.
Scaling Dimension
Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond.
Conceptual Views on Tree Ensemble Classifiers
Random Forests and related tree-based methods are popular for supervised learning from table based data.
Discovering Locally Maximal Bipartite Subgraphs
Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs.
Intrinsic Dimension for Large-Scale Geometric Learning
In the present work, we derive a computationally feasible method for determining said axiomatic ID functions.
Formal Conceptual Views in Neural Networks
Explaining neural network models is a challenging task that remains unsolved in its entirety to this day.
Research Topic Flows in Co-Authorship Networks
Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows.
Mapping Research Trajectories
Hence, we propose a principled approach for \emph{mapping research trajectories}, which is applicable to all kinds of scientific entities that can be represented by sets of published papers.
Towards Explainable Scientific Venue Recommendations
Selecting the best scientific venue (i. e., conference/journal) for the submission of a research article constitutes a multifaceted challenge.
Topological Indoor Mapping through WiFi Signals
The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping.
Quantifying the Conceptual Error in Dimensionality Reduction
Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning.
Exploring Scale-Measures of Data Sets
Measurement is a fundamental building block of numerous scientific models and their creation.
On the Lattice of Conceptual Measurements
We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i. e., continuous maps between closure systems, and derive a canonical representation.
Topic Space Trajectories: A case study on machine learning literature
In this task, researchers can be supported by automated publication analysis.
Knowledge Cores in Large Formal Contexts
Knowledge computation tasks are often infeasible for large data sets.
FCA2VEC: Embedding Techniques for Formal Concept Analysis
Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets.
Orometric Methods in Bounded Metric Data
Notably, metric sets of items inclosed in knowledge graphs.
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.
DimDraw -- A novel tool for drawing concept lattices
Concept lattice drawings are an important tool to visualize complex relations in data in a simple manner to human readers.
Discovering Implicational Knowledge in Wikidata
Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world.
Relevant Attributes in Formal Contexts
Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task.
Formal Context Generation using Dirichlet Distributions
We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions.
Distances for WiFi Based Topological Indoor Mapping
For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator.
Probably approximately correct learning of Horn envelopes from queries
We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain.
Intrinsic dimension and its application to association rules
This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures.
Intrinsic Dimension of Geometric Data Sets
The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension.
Towards Collaborative Conceptual Exploration
Using notions from combinatorial design theory we further expand those insights as far as providing first results on the decidability problem if a given consortium is able to explore some target domain.
On the Usability of Probably Approximately Correct Implication Bases
We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use-cases.
