Читать книгу Deep Learning Approaches to Cloud Security - Группа авторов - Страница 21

Feature extraction extracts distinct features from samples represented in a feature vector. Thus, alteration of attributes takes place in this method, whereas predictive significance criteria is used to rank the current attributes in feature selection technique. The altered features or attributes are linear aggregations of the original attributes. Since data is described by a lesser number of meaningful features, we obtain a higher quality model based on such derived attributes [12].

Feature extraction helps in data visualization by reducing a complex data set to 2 or 3 dimensions. It can improve the speed and efficiency of supervised learning. Feature extraction can also be used to enhance the speed and effectiveness of supervised learning. It has applications in data compression, data decomposition and projection, latent semantic analysis, and pattern recognition.

The information is extended onto the largest Eigen Vectors in order to reduce the dimensionality.

Let V = matrix with columns having the largest Eigen Vectors and

D = original data with columns of different observations.

Then, the projected data D′ is derived as D′ = V^T D.

In the event when just N Eigen Vectors are kept and e1...eN represents the related Eigen Values, the amount of variance left after projecting the original d-dimensional data can be determined as: