Data Preprocessing Questions
The common techniques used for feature extraction in data preprocessing include:
1. Principal Component Analysis (PCA): It is a statistical technique that reduces the dimensionality of the data by transforming it into a new set of variables called principal components. These components capture the maximum amount of information from the original data.
2. Independent Component Analysis (ICA): It is a computational method that separates a multivariate signal into additive subcomponents. It assumes that the observed data are a linear combination of independent sources and aims to recover these sources.
3. Linear Discriminant Analysis (LDA): It is a dimensionality reduction technique that maximizes the separation between different classes in the data. It finds a linear combination of features that best discriminates between classes.
4. Non-negative Matrix Factorization (NMF): It is a method that decomposes a non-negative matrix into the product of two lower-rank non-negative matrices. NMF is often used for feature extraction in text mining and image processing tasks.
5. Wavelet Transform: It is a mathematical technique that decomposes a signal into different frequency components. It is particularly useful for analyzing signals with varying frequencies over time, such as audio and image data.
6. Bag-of-Words (BoW): It is a technique commonly used in natural language processing to represent text data. It converts text documents into a matrix of word frequencies, disregarding the order and structure of the words.
7. Histogram of Oriented Gradients (HOG): It is a feature extraction technique commonly used in computer vision tasks, such as object detection. It calculates the distribution of gradient orientations in an image to capture shape and edge information.
These techniques help in reducing the dimensionality of the data, extracting relevant information, and improving the performance of machine learning algorithms.