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In the past, I’ve covered how to implement k-means and how to cluster MNIST directly. Then I showed a bad way to reduce dimensionality for clustering. Finally, we used an autoencoder on MNIST. In this post, we’ll join everything together by performing clustering on the encoding and see how it performs. In the previous post, […]
Clustering on encoding Read More »
Dimensionality reduction is useful in compressing the representation of your data. In my last post, how not to reduce dimensionality for clustering, I show a neural network that reduces the 784 dimensions of the MNIST dataset into 3, which are constrained by the k-means algorithm to capture the essence of the data. This week we’ll
Reduce MNIST dimensionality using autoencoders Read More »
In my previous post, I followed up on my k-means tutorial by applying it to cluster MNIST. We’re used to reading digits, so the centroids made perfect sense to us since plotting them just looked like a smudged number. The dataset even clustered pretty well since all digits are centred and look similar enough that
How not to reduce dimensionality for clustering Read More »
This is the beginning of a series in which we’ll explore ways to perform clustering. In particular, we’ll perform clustering on the MNIST dataset, since it’s a dataset of high dimensionality which is easy to understand. In my previous post, ‘How to implement the K-means algorithm in Tensorflow‘, I explain the code of Sergey Kovalev,
Clustering MNIST – naïve Read More »