Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
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Updated
Mar 5, 2024 - Python
Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
A Python toolbox for gaining geometric insights into high-dimensional data
High-dimensional medians (medoid, geometric median, etc.). Fast implementations in Python.
Deep distance-based outlier detection published in KDD18: Learning representations specifically for distance-based outlier detection. Few-shot outlier detection
A Python package for hubness analysis and high-dimensional data mining
A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets (JASA-20 paper)
Implementation of NEWMA: a new method for scalable model-free online change-point detection
MinMax Circular Sector Arc for External Plagiarism’s Heuristic Retrieval Stage code
Hubness analysis and removal functions
Python scripts from paper Optimal cleaning for singular values of cross-covariance matrices, by Florent Benaych-Georges, Jean-Philippe Bouchaud, Marc Potters (see https://arxiv.org/abs/1901.05543)
A small library for regridding Earth system data with vectorized sparse linear transforms
python library to perform Locality-Sensitive Hashing for faster nearest neighbors search in high dimensional data
J3 for Python - Launches the J3 viewer from Python
Implementation of hierarchical clustering on small n-sample dataset with very high dimension. Together with the visualization results implemented in R and python
High dimensional indexing of image data using rtree, implemented to find similar images in the indexed dataset
A Toolbox for Dynamic Mapping in Python
This repository contains code for MFmap (model fidelity map), a semi-supervised generative model integrating gene expression, copy number and mutation data, matching cell lines to cancer subtypes. MFmap compresses high dimensional omics data of cell lines and bulk tumours into subtype informative low dimensional latent representations and predic…
Code for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"
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