As PCA and linear autoencoder have a close relation, this post introduces again PCA as a powerful dimension reduction tool while skipping many mathematical proofs. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly...
This post introduces using linear autoencoder for dimensionality reduction using TensorFlow and Keras. What is a linear autoencoder An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network...
The most basic yet important thing when working with data array is its dimensions. This article will cover several data shapes and reshaping techniques. Why need reshaping data Imagine that you are starving and suddenly given a piece of delicious food. You may try to put it all in your mouth (Fig 1a) and find out it cannot help your hunger. So, you decided to arrange your food so that it not only...