In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling.
In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.
In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction.
In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
Die Grundannahme für die Verwendung der PCA zur Clusteranalyse und Dimensionsreduktion lautet: Die Richtungen mit der größten Streuung (Varianz) beinhalten die meiste Information.
The Matlab Toolbox for Dimensionality Reduction contains Matlab implementations of a large number of techniques for dimensionality reduction. A large number of implementations was developed from scratch, whereas other implementations are improved versions
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Daisuke Mitomo, Hironori K. Nakamura, Kazuyoshi Ikeda, Akihiko Yamagishi, Junichi Higo
Published Online: Jun 28 2006 4:14PM
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Theoretical investigation of the photoinitiated folding of HP-36
Protein Sci 2006 15: 2290-2299. Published in Advance September 8, 2006, 10.1110/ps.062145106.