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Found 6 papers, 3 papers with code
Topological Analysis for Detecting Anomalies (TADA) in Time Series
This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels.
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning frameworks.
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science.
Adaptive Clustering through Semidefinite Programming
We analyze the clustering problem through a flexible probabilistic model that aims to identify an optimal partition on the sample X1,..., Xn.
PECOK: a convex optimization approach to variable clustering
The problem of variable clustering is that of grouping similar components of a $p$-dimensional vector $X=(X_{1},\ldots, X_{p})$, and estimating these groups from $n$ independent copies of $X$.
Statistics Theory Statistics Theory
Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
We quantify the difficulty of clustering data generated from a G-block covariance model in terms of cluster proximity, measured with respect to two related, but different, cluster separation metrics.
