 The resulting 30 dimensions of the code yielded a smaller reconstruction error compared to the first 30 principal components of a PCA, and learned a representation that was qualitatively easier to interpret, clearly separating clusters in the original data.Representing data in a lower-dimensional space can improve performance on different tasks, such as classification.Indeed, many forms of dimensionality reduction place semantically related examples near each other, aiding generalization.