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...
This must be done avoiding a phenomenon known as the curse of dimensionality, that appears in Machine Learning when algorithms must learn from an ample feature volume with abundant values within each one.Hidalgo-Mompean, FernandoFernandez, Juan Francisco GomezCerruela-Garcia, GonzaloMarquez, Adolfo ...
This also follows the “No Lunch Theorem” principle in some sense: there is no method that is always superior; it depends on your dataset. Intuitively, LDA would make more sense than PCA if you have a linear classification task, but empirical studies showed that it is not always the case...
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...
For many large-scale applications in data mining, machine learning, and multimedia, fundamental operations such as similarity search, retrieval, classification, clustering, and anomaly detection generally suffer from an effect known as the `curse of dimensionality'. As the dimensionality of the data in...
arise in high-dimensional space. In Machine Learning, one common manifestation is the fact that randomly sampled high dimensional vectors are generally very sparse, increasing the risk of overfitting and making it very difficult to identify patterns in the data without having plenty of training data...
The recent uprise of Knowledge Discovery in Databases (KDD) has underlined the need for machine learning algorithms to be able to tackle largescale applications that are currently beyond their scope. One way to address this problem is to use techniques for reducing the dimensionality of the learnin...
Margin is one of the most important concepts in machine learning. Previous margin bounds, both for SVM and for boosting, are dimensionality independent. A major advantage of this dimensionality independency is that it can explain the excellent performance of SVM whose feature spaces are often of hi...
摘要: Dimensionality reduction is an important task in machine learning, for it facilitates classification, compression, and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Over the last decade, a large number of...
Machine learning in cybersecurity: a comprehensive survey Today's world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices ... D Dasgupta,Z Akhtar,S Sen - 《Journal of Defense Modeling & Simulation》 被...