FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

Overview

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well in noisy and contaminated datasets.

Authors

Andrew Wang, University of Cambridge, Cambridge, UK Pierre Houdouin, CentraleSupélec, Paris, France

Instllation

pip install -i https://test.pypi.org/simple/ femda

Get started

>>> from sklearn.datasets import load_iris
>>> from femda import FEMDA
>>> X, y = load_iris(return_X_y=True)
>>> clf = FEMDA()
>>> clf.fit(X, y)
FEMDA()
>>> clf.score(X, y)
0.9666666666666667

Using a specific dataset...

>> FEMDA().fit(X_train, y_train).score(X_test, y_test) ...">
>>> import femda.experiments.preprocessing as pre
>>> X_train, y_train, X_test, y_test = pre.statlog(r"root\datasets\\")
>>> FEMDA().fit(X_train, y_train).score(X_test, y_test)
...

Using a sklearn.pipeline.Pipeline...

>>> from sklearn.datasets import load_digits
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.decomposition import PCA
>>> X, y = load_digits(return_X_y=True)
>>> pipe = make_pipeline(PCA(n_components=5), FEMDA()).fit(X, y)
>>> pipe.predict(X)
...

Run all experiments presented in the paper

>>> from femda.experiments import run_experiments()
>>> run_experiments()
...

See demo.ipynb for more.

Abstract

Linear and Quadraic Discriminant Analysis are well-known classical methods but suffer heavily from non-Gaussian class distributions and are very non-robust in contaminated datasets. In this paper, we present a new discriminant analysis style classification algorithm that directly models noise and diverse shapes which can deal with a wide range of datasets.

Each data point is modelled by its own arbitrary Elliptically Symmetrical (ES) distribution and its own arbitrary scale parameter, modelling directly very heterogeneous, non-i.i.d datasets. We show that maximum-likelihood parameter estimation and classification are simple and fast under this model.

We highlight the flexibility of the model to a wide range of Elliptically Symmetrical distribution shapes and varying levels of contamination in synthetic datasets. Then, we show that our algorithm outperforms other robust methods on contaminated datasets from Computer Vision and NLP.

Single/multi view image(s) to voxel reconstruction using a recurrent neural network

3D-R2N2: 3D Recurrent Reconstruction Neural Network This repository contains the source codes for the paper Choy et al., 3D-R2N2: A Unified Approach f

Chris Choy 1.2k Dec 27, 2022
Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

PackNet: https://arxiv.org/abs/1711.05769 Pretrained models are available here: https://uofi.box.com/s/zap2p03tnst9dfisad4u0sfupc0y1fxt Datasets in Py

Arun Mallya 216 Jan 05, 2023
Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Dominic Rampas 247 Dec 16, 2022
The repository is for safe reinforcement learning baselines.

Safe-Reinforcement-Learning-Baseline The repository is for Safe Reinforcement Learning (RL) research, in which we investigate various safe RL baseline

172 Dec 19, 2022
OverFeat is a Convolutional Network-based image classifier and feature extractor.

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

593 Dec 08, 2022
PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric

PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric This repository contains the implementation of MSBG hearing loss m

BUT <a href=[email protected]"> 9 Nov 08, 2022
An implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks in PyTorch.

Neural Attention Distillation This is an implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep

Yige-Li 84 Jan 04, 2023
The codebase for Data-driven general-purpose voice activity detection.

Data driven GPVAD Repository for the work in TASLP 2021 Voice activity detection in the wild: A data-driven approach using teacher-student training. S

Heinrich Dinkel 75 Nov 27, 2022
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Adversarial Training Against Location-Optimized Adversarial Patches arXiv | Paper | Code | Video | Slides Code for the paper: Sukrut Rao, David Stutz,

Sukrut Rao 32 Dec 13, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
ML-Ensemble – high performance ensemble learning

A Python library for high performance ensemble learning ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framew

Sebastian Flennerhag 764 Dec 31, 2022
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

Aitor Ruano 87 Dec 16, 2022
Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

Point-Unet This is an unofficial implementation of the MICCAI 2021 paper Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segment

Namt0d 9 Dec 07, 2022
Apache Spark - A unified analytics engine for large-scale data processing

Apache Spark Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an op

The Apache Software Foundation 34.7k Jan 04, 2023
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma 🔥 News 2021-10

Jingtao Zhan 99 Dec 27, 2022
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

195 Dec 07, 2022
Controlling a game using mediapipe hand tracking

These scripts use the Google mediapipe hand tracking solution in combination with a webcam in order to send game instructions to a racing game. It features 2 methods of control

3 May 17, 2022
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels The implementation of Hypernet-Ensemble Le

Sungmin Hong 6 Jul 18, 2022
Code for one-stage adaptive set-based HOI detector AS-Net.

AS-Net Code for one-stage adaptive set-based HOI detector AS-Net. Mingfei Chen*, Yue Liao*, Si Liu, Zhiyuan Chen, Fei Wang, Chen Qian. "Reformulating

Mingfei Chen 45 Dec 09, 2022