Reconstruction from Compressed Repressed Representation 上一节中,我们获取了原数据在新特征下的一个低维表示z^{(i)} \in \mathbb{R}^{k \times 1}。PCA是一种数据压缩算法,那么我们应该也能够通过低维数据回到高维数据或是近似值。 如图中,我们假设样本点x^{(i)} \in \mathbb{R}^{n \times 1}压缩...
本笔记为Coursera在线课程《Machine Learning》中的数据降维章节的笔记。 十四、降维(Dimensionality Reduction) 14.1动机一:数据压缩 本小节主要介绍第二种无监督学习方法:dimensionality reduction,从而实现数据的压缩,这样不仅可以减少数据所占磁盘空间,还可以提高程序的运行速度。如下图所示的例子,假设有一个具有很多维特...
Machine Learning - Dimensionality Reduction - Dimensionality reduction in machine learning is the process of reducing the number of features or variables in a dataset while retaining as much of the original information as possible. In other words, it is
The goal of machine learning algorithm is to understand the basic features of a complex system. If the dataset is large and the number of features is large as well, it is possible that one can get features or input variables easily identified. In case the dataset is small, there may be ...
2. Unsupervised Learning(无监督学习) 与监督学习相反,训练集中的数据并没有标签,这意味着你需要从这堆没有标签的数据中去提炼它们的特点规则等等,可能是通过数学推理过程来系统地减少冗余,又或者是通过数据相似度来组织数据。 经典问题:clustering, dimensionality reduction and association rule learning.(聚类、降维...
(原创)Stanford Machine Learning (by Andrew NG) --- (week 8) Clustering & Dimensionality Reduction 本周主要介绍了聚类算法和特征降维方法,聚类算法包括K-means的相关概念、优化目标、聚类中心等内容;特征降维包括降维的缘由、算法描述、压缩重建等内容。coursera上面Andrew NG的Machine learning课程地址为:https:/...
Machine Learning FAQ Since there are so many different approaches, let’s break it down to “feature selection” and “feature extraction.” Some examples of feature selection: L1 regularization (e.g., Logistic regression) and sparsity variance thresholds...
Supervised Learning Unsupervised Learning Clustering K-means Algorithm K-means算法是无监督学习算法的一种,下图所示,K表示期望将数据分成的类别个数. input 下面这个例子是使用K-means算法将Training Set分为两类. Training Set 因为要分成两类,所以随机初始化两个cluster centroids, red/blue ...
Types of Machine Learning - Which One is Right for You? Machine Learning Datasets for Every Industry Data Preprocessing in Machine Learning: A Comprehensive Guide Machine Learning Algorithms - A Complete Guide Classification Machine Learning - A Comprehensive Guide SVM Algorithm in Python and Machine ...
Learning algorithms generally require that database be described in terms of a set of measurable features. Feature extraction is the process of driving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency and allow higher classificati...