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快速K-均值(kmeans)聚类图像分割算法源代码
- 本算法Kmeans可以用于非监督分类学习，用于图像处理、模式识别分类(The algorithm Kmeans can be used for unsupervised classification learning, for image processing, pattern recognition and classification.)
compute_mapping
- 输入： 二维矩阵；输出：降维结果； 共包含34种降维方法，线性/非线性；局部/全局；监督/非监督(Input: 2-D matrix; output: dimension reduction result; contains 34 dimensionality reduction methods, linear / nonlinear; local / global; supervised / unsupervised.)
processing
- 包括遥感图像分类（监督和非监督）、分类后处理、NDVI、波段运算、颜色转换、光谱分析等等。(It includes remote sensing image classification (supervised and unsupervised), post-processing classification, NDVI, band operation, color conversion, spectral analysis and so on.)
Span_H_Alpha分类
- 参照论文《基于SPAN／H／alpha／A和复Wishart分割的全极化SAR数据的非监督分类算法研究》(Referenced research on unsupervised classification algorithm of fully polarimetric SAR data based on SPAN / H / alpha / A and complex Wishart segmentation)
MATLAB and Machine learning
- MATLAB与机器学习，包含机器学习简介，快速入门，应用监督式学习，应用无监督学习(MATLAB and Machine Learning, including Machine Learning Introduction, Quick Start, Application Supervised Learning, Application Unsupervised Learning)
DBN
- 深度信念网络，神经网络的一种。既可以用于非监督学习，类似于一个自编码机；也可以用于监督学习，作为分类器来使用。(Deep belief network, a kind of neural network. It can be used for unsupervised learning, similar to a self-coding machine, or supervised learning, as a classifier.)
short-term-clustering
- UNSUPERVISED LOCATION-BASED SEGMENTATION OF MULTI-PARTY SPEECH的配套源码,G. Lathoud, I.A. McCowan and J.M. Odobez
som
- 随机产生5类二维坐标系中的数，使用SOM网络进行无监督聚类，将产生的随机数自动聚成五类，并将结果用图像直接显示出来，生成训练好的网络权值(Five kinds of random numbers in two-dimensional coordinate system are generated randomly, and unsupervised clustering is carried out using SOM network. The random numbers generated
稀疏自动编码器的matlab代码
- 本资源是3层的自编码器加上稀疏正则项约束的matlab代码。隐层激活函数选sigmoid函数，输出层选线性函数，程序中以一个标准数据集sonar为例，使用该方法可以做无监督表征学习，数据压缩，多任务学习等(This resource is a 3-layer self-encoder plus matlab code for sparse regular term constraints. The hidden layer activation function selects the sigm
lesson51-WGAN实战
- 生成式对抗网络（GAN, Generative Adversarial Networks ）是一种深度学习模型，是近年来复杂分布上无监督学习最具前景的方法之一。模型通过框架中（至少）两个模块：生成模型（Generative Model）和判别模型（Discriminative Model）的互相博弈学习产生相当好的输出。(Emergent against network (GAN, Generative Adversarial Networks) is a kind of deep learni
Approximate low-rank projection1
- 在文中，提出来一个基于低秩的特征提取方法(Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised featu
SuperPCA-master
- 高光谱图像无监督特征提取的超像素PCA方法(A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery)
吴恩达深度学习基础教程
- 本教程将阐述无监督特征学习和深入学习的主要观点。通过学习，你也将实现多个功能学习/深度学习算法，能看到它们为你工作，并学习如何应用/适应这些想法到新问题上。(This tutorial will explain the main points of unsupervised feature learning and in-depth learning. Through learning, you will also implement multiple functional learning /
k_means
- 利用K均值算法对Iris数据集进行聚类，实现Iris数据集的无监督学习。(K-means algorithm is used to cluster iris data set to realize unsupervised learning.)
kmeans聚类算法
- kmeans聚类分析，无监督学习实现Matlab代码(Kmeans clustering analysis, unsupervised learning implementation of MATLAB code)