![]() Johnson-Lindenstrauss lemma (quoting Wikipedia): The main theoretical result behind the efficiency of random projection is the Knowledge discovery and data mining (KDD ‘01). In Proceedings of the seventh ACM SIGKDD international conference on Random projection in dimensionality reduction: applications to image and text data. Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151. Intelligence (UAI’00), Craig Boutilier and Moisés Goldszmidt (Eds.). In Proceedings of the Sixteenth conference on Uncertainty in artificial Thus random projection is a suitable approximation ![]() The dimensions and distribution of random projections matrices areĬontrolled so as to preserve the pairwise distances between any two Processing times and smaller model sizes. Trading a controlled amount of accuracy (as additional variance) for faster The sklearn.random_projection module implements a simple andĬomputationally efficient way to reduce the dimensionality of the data by
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