Implementation of the block-descent-CoCoLasso, inspired from the article https://arxiv.org/pdf/1510.07123.pdf
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Updated
Feb 5, 2020 - R
Implementation of the block-descent-CoCoLasso, inspired from the article https://arxiv.org/pdf/1510.07123.pdf
A new efficient subspace and K-Means clustering based method to improve Collaborative Filtering
Modelos de alta dimensionalidade para previsão do IPCA
Robust and fast Monte Carlo algorithm for high dimension integration
Clustered heatmap of plant densities and multiple related variables in gardens
Quantum neural network research implementing multi-dimensional neuron representations. Explores theoretical integration of quantum computing principles into neural systems to investigate emergent cognition and consciousness.
A Case study is to classify the genetic mutation classes.
In this project I look at the high dimensional MNIST dataset of handwritten digits and use PCA, t-SNE and Topological data analysis (TDA) to visualise and understand the dataset.
CVaR Portfolio Optimization in High Dimensions
This repository contains the R-Package for a novel time series forecasting method designed to handle very large sets of predictive signals, many of which are irrelevant or have only short-lived predictive power.
An Efficeint and Fast Wrapper-based High-dimensional Feature Selection(SIFE) in MATLAB
CFOF developed in Python. Based on Angiulli's works : https://arxiv.org/pdf/1901.04992v2.pdf
Efficient Learning of Minimax Risk Classifiers in High Dimensions
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