75 Open source projects that are alternatives of or similar to pymde

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".

A curated list of community detection research papers with implementations.

OpenANE: the first Open source framework specialized in Attributed Network Embedding. The related paper was accepted by Neurocomputing. https://doi.org/10.1016/j.neucom.2020.05.080

An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)

Mutli-label text classification using ConvNet and graph embedding (Tensorflow implementation)

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

Graph Embedding via Frequent Subgraphs

🕝 Time-warped principal components analysis (twPCA)

Local Fisher Discriminant Analysis in R

Uncertainty-aware principal component analysis.

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

Mandrake 🌿/👨‍🔬🦆 – Fast visualisation of the population structure of pathogens using Stochastic Cluster Embedding

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

*SEM 2018: Learning Distributed Event Representations with a Multi-Task Approach

TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses

Position embedding layers in Keras

Go Embed experiments

Some Cool NLP and CV Repositories and Solutions （收集NLP中常见任务的开源解决方案、数据集、工具、学习资料等）

Embed media and rich content in posts: YouTube, Vimeo, Twitch and more.

Uniform Manifold Approximation and Projection

A Tensorflow retrieval (space embedding) baseline. Metric learning baseline on CUB and Stanford Online Products.

Online feature-extraction and classification algorithm that learns representations of input patterns.

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

Tri-Party Deep Network Representation, IJCAI-16

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

A Python Library for Similarity-based Dimensionality Reduction

Ensemble topic modelling with pLSA

A fast and flexible framework for data reduction in R

A simple wrapper class for extracting features(embedding) and comparing them using BERT in TensorFlow

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

PyTorch implementation of Deep Randomized Ensembles for Metric Learning(ECCV2018)

Single-cell Hierarchical Poisson Factorization

Easy genetic ancestry predictions in Python

Streaming, Memory-Limited, r-truncated SVD Revisited!

A multi-thread implementation of node2vec random walk.

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

Multi-Hop Logical Reasoning in Knowledge Graphs

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

A comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.

tGPLVM: A Nonparametric, Generative Model for Manifold Learning with scRNA-seq experimental data

Federated Principal Component Analysis Revisited!

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

Comparison of dimensionality reduction methods

A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

R package for Distance Metric Learning

A repository of resources for understanding the concepts of machine learning/deep learning.

Parallel Barnes-Hut t-SNE implementation written in Rust.

A list of data mining and machine learning papers that I implemented in 2019.

Learning node representation using edge semantics

In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.

Code for the paper "A Similarity Measure for Material Appearance" presented in SIGGRAPH 2019 and published in ACM Transactions on Graphics (TOG).

The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".

中文文本分类实践，基于搜狗新闻语料库，采用传统机器学习方法以及预训练模型等方法