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Your algorithm may work only if embeddings created by the manifold learning (T-SNE) catch information that the features by themselves do not. As mentioned in the comments, if you use T-SNE, you will h …

To understand how the original features contribute to these principal components, you can examine the components_ attribute of the fitted PCA object in scikit-learn. … Here is an example: from sklearn.decomposition import PCA # Assume X is your data pca = PCA(n_components=3) pca.fit(X) print(pca.components_) The output might look something like this: [[ 0.5 -0.1 …

First of all, a picture should not be taken to define if there are or no groups on your data, since no matter what projection you use (linear with PCA or manifold with tSNE), you are reducing a 64-dimensional … two pieces of advice to validate if there are such groups in your data: You can try using a projection algorithm before clustering like you already have, but I recommend using UMAP instead of tSNE or PCA …

PlotlyExpress instead This code is plotting the first 3 components on the iris dataset import plotly.express as px from sklearn.datasets import load_iris from sklearn.decomposition import PCA … ()), ("pca",PCA(n_components = 3)), ("dataframe", FunctionTransformer(lambda x: pd.DataFrame(x, columns = ["first_comp", "second_comp", "third_comp"])))]).fit(X) X3D = pca.transform(X) px.scatter …

If you want to reduce the number of features via some decomposition technique PCA won't be adequate since the term-frequency matrix is sparse, so you could, for example, using NMF (Non-negative matrix …