Table of Contents
What is reconstruction error?
The general definition of the reconstruction error would be the distance between the original data point and its projection onto a lower-dimensional subspace (its ‘estimate’).
What is the meaning of cross-entropy?
Cross-entropy is a measure of the difference between two probability distributions for a given random variable or set of events. You might recall that information quantifies the number of bits required to encode and transmit an event.
What is the meaning of cross-entropy loss?
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. As the predicted probability decreases, however, the log loss increases rapidly.
Is cross-entropy a log likelihood?
Here is the crucial difference between the two cost functions: the log-likelihood considers only the output for the corresponding class, whereas the cross-entropy function also considers the other outputs as well.
How do you find the reconstruction error?
One way to calculate the reconstruction error from a given vector is to compute the euclidean distance between it and its representation. In K-means, each vector is represented by its nearest center.
Can cross entropy be more than 1?
Mathematically speaking, if your label is 1 and your predicted probability is low (like 0.1), the cross entropy can be greater than 1, like losses.
What is the difference between log loss and cross entropy?
Log loss is usually used when there are just two possible outcomes that can be either 0 or 1. Cross entropy is usually used when there are three or more possible outcomes. In words, cross entropy is the negative sum of the products of the logs of the predicted probabilities times the actual probabilities.
Why do we minimize cross entropy?
Cross-entropy loss is used when adjusting model weights during training. The aim is to minimize the loss, i.e, the smaller the loss the better the model.
What is reconstruction error in Autoencoder?
Reconstruction error is the distance between the original input and its autoencoder reconstruction. Autoencoders compress the input into a lower-dimensional projection and then reconstruct the output from this representation.
What is PCA and how does it work?
Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance.