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Found 6 papers, 2 papers with code
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset.
Machine Learning Methods for Device Identification Using Wireless Fingerprinting
Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards.
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering.
Hub-aware Random Walk Graph Embedding Methods for Classification
In this paper, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem.
Local Intrinsic Dimensionality Measures for Graphs, with Applications to Graph Embeddings
The notion of local intrinsic dimensionality (LID) is an important advancement in data dimensionality analysis, with applications in data mining, machine learning and similarity search problems.
The Influence of Global Constraints on Similarity Measures for Time-Series Databases
In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them.
