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Posts tagged "PCA" In this article, we discuss how Principal Component Analysis (PCA) works, and how it can be used as a dimensionality reduction technique for classification problems. At the end of this article, Matlab source code is provided for demonstration purposes. In an earlier article, we discussed the so called...
Continue reading » May 16, 2014 Vincent SpruytFeature extraction14 Comments In this article, we provide an intuitive, geometric interpretation of the covariance matrix, by exploring the relation between linear transformations and the resulting data covariance. Most textbooks explain the shape of data based on the concept of covariance matrices. Instead, we take a backwards approach and explain the...
Continue reading » April 24, 2014 Vincent SpruytLinear algebra47 Comments In this article, we will discuss the so called ‘Curse of Dimensionality’, and explain why it is important when designing a classifier. In the following sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality....
Continue reading » April 16, 2014 Vincent SpruytFeature extraction52 Comments Eigenvectors and eigenvalues have many important applications in computer vision and machine learning in general. Well known examples are PCA (Principal Component Analysis) for dimensionality reduction or EigenFaces for face recognition. An interesting use of eigenvectors and eigenvalues is also illustrated in my post about error ellipses. Furthermore,...
Continue reading » March 5, 2014 Vincent SpruytLinear algebra20 Comments 
