Sparse Representation (SR) Toolbox

Application Background: This toolbox implements machine learning methodologies including sparse coding-based classification, dictionary-based dimensionality reduction with sub-dictionary learning, learning models, and linear regression/classification (LRC). It features implementations of kernel l1-regularized and/or non-negative constrained sparse coding and dictionary learning models. Key Technologies: The optimization utilizes active set, interior point, proximal, and decomposition methods. Current version: 1.9 (March 2, 2015). Freely available for academic use with commercial licenses offering advanced features and technical support.

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Machine Learning Source Code for Dimensionality Reduction, Feature Selection, and Related Algorithms

Comprehensive machine learning source code authored by Zhejiang University professors Cai Deng and He Xiaofei, covering spectral regression, dimensionality reduction, feature selection, topic modeling, matrix factorization, sparse coding, hashing techniques, clustering methods, active learning, and matrix learning. This collection serves as an excellent resource for understanding algorithm implementations through practical code examples.

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MATLAB Machine Learning

Explore various machine learning techniques available in MATLAB, including how to quickly explore data, evaluate machine learning algorithms, compare results, and apply the optimal machine learning approach to your specific problems. The focus covers both unsupervised and supervised machine learning techniques, featuring: • K-means and other clustering tools with cluster evaluation methods • Neural networks with architecture customization options • Decision trees and ensemble learning with boosting capabilities • Naive Bayes classification with probability estimation features • Linear, logistic, and nonlinear regression with model fitting functions

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MATLAB Machine Learning Code Implementation

Comprehensive MATLAB source code for machine learning, featuring multi-class SVM algorithms, pattern recognition systems, feature selection methods, and various regression techniques with practical implementation examples.

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Locality Preserving Projections (LPP) for Nonlinear Dimensionality Reduction

Locality Preserving Projections (LPP) as a nonlinear dimensionality reduction method can be effectively applied to machine learning with high-dimensional datasets using graph-based neighborhood preservation techniques.

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Demonstration of Machine Learning: Q-Learning Algorithm

This article focuses on Q-learning algorithm implementation, where readers can conduct their own research via Baidu or reference technical papers for deeper understanding.

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MATLAB Implementation of Recommendation System with Machine Learning

MATLAB-based recommendation system integrating machine learning approaches, originally developed by Stanford University's Andrew Ng. Features collaborative filtering algorithms and optimization techniques for personalized recommendations.

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Banana-Shaped Standard Dataset

The banana-shaped standard dataset is designed for testing machine learning and pattern recognition algorithms, featuring comprehensive image variations for robust algorithm evaluation.

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EM Algorithm: A Fundamental Approach in Machine Learning

The EM algorithm is a widely used technique in machine learning. This implementation demonstrates its most basic form applied to Gaussian Mixture Models, featuring clear code structure with separate E-step and M-step functions for educational purposes.

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Machine Learning Course Assignment: PCA, LDA Dimensionality Reduction with Naive Bayes Classifier Comparison

A machine learning course assignment implementing PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) for dimensionality reduction. Unlike many online resources with sparse comments, this implementation includes comprehensive annotations and attention to implementation details. Features a comparative Naive Bayes classifier and uses the OLR face image dataset. Important: ReducedDim parameter specifies the exact number of features to extract, not a percentage.

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