# single layer autoencoder performing a lot worse than pca

I am trying to use a single layer autoencoder with linear activation function to perform dimensionality reduction on a dataset before clustering.

The data consists of 5000 samples with 2000 features each. I'm using keras (tensorflow backend). The model that I am using is returned by the function below

    model = Sequential()

red_dim_model = Model(inputs=model.input,  outputs=model.get_layer('reduce_layer').output)