Xiaoyang-Rebecca / Patternrecognition_matlab
Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).
Stars: ✭ 33
Programming Languages
matlab
3953 projects
Projects that are alternatives of or similar to Patternrecognition matlab
Machine-Learning-Models
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Stars: ✭ 30 (-9.09%)
Mutual labels: svm, pca, lda
Machine Learning With Python
Python code for common Machine Learning Algorithms
Stars: ✭ 3,334 (+10003.03%)
Mutual labels: svm, pca, lda
Svm Classification Localization
HoG, PCA, PSO, Hard Negative Mining, Sliding Window, Edge Boxes, NMS
Stars: ✭ 130 (+293.94%)
Mutual labels: svm, pca
Isl Python
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Stars: ✭ 108 (+227.27%)
Mutual labels: pca, lda
Nlp Journey
Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0.
Stars: ✭ 1,290 (+3809.09%)
Mutual labels: svm, lda
Facerecognition
Implement face recognition using PCA, LDA and LPP
Stars: ✭ 206 (+524.24%)
Mutual labels: pca, lda
Ml Course
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
Stars: ✭ 154 (+366.67%)
Mutual labels: svm, pca
Quick-Data-Science-Experiments-2017
Quick-Data-Science-Experiments
Stars: ✭ 19 (-42.42%)
Mutual labels: pca, lda
NIDS-Intrusion-Detection
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
Stars: ✭ 45 (+36.36%)
Mutual labels: svm, pca
NMFADMM
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Stars: ✭ 39 (+18.18%)
Mutual labels: pca, lda
Ailearning
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Stars: ✭ 32,316 (+97827.27%)
Mutual labels: svm, pca
Jsat
Java Statistical Analysis Tool, a Java library for Machine Learning
Stars: ✭ 683 (+1969.7%)
Mutual labels: svm
Digital Image Processing Algorithms
SJTU CS386 Digital Image Processing
Stars: ✭ 20 (-39.39%)
Mutual labels: pca
H2o 3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Stars: ✭ 5,656 (+17039.39%)
Mutual labels: pca
Prince
👑 Python factor analysis library (PCA, CA, MCA, MFA, FAMD)
Stars: ✭ 591 (+1690.91%)
Mutual labels: pca
Plnmodels
A collection of Poisson lognormal models for multivariate count data analysis
Stars: ✭ 31 (-6.06%)
Mutual labels: pca
PatternRecognition_Matlab
Abstract
Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).
Conclusion
Our experiments showed that SVM was the most robust method to increase dimensional space, and that SVM and LDA were the most sensitive to noise.
Documentations
Code Run Instruction
Input data : data
Main function : mainFCT.m
About author
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].