Jakob Verbeek
Software Here's a collection of Matlab scripts available for non-commercial use. Please email for questions & suggestions. K-means A compact and efficient kmeans implementation. 12/7/2011 [Matlab implementation] Coordinated Factor Analysis A MATLAB implementation of the Coordinated Factor Analysis (CFA) model described in my 2006 PAMI paper can be found in the Matlab Toolbox for Dimensionality Red
Blom and McCoy - pLSI and NMF
Comparing pLSI and Non-negative Matrix Factorization This is the homepage for Zack McCoy and Martin Blom for CS395T Data Mining: A Statistical Learning Perspective, Spring 2007. We've completed our project and all the relevant files are below. A quick note: All the code is in Python and requires scipy, numpy, and pylab, all of which you can download from www.scipy.org The main code file is nmfplsa
Latent Semantic Analysis in Python - Joseph Wilk
Latent Semantic Analysis (LSA) is a mathematical method that tries to bring out latent relationships within a collection of documents. Rather than looking at each document isolated from the others it looks at all the documents as a whole and the terms within them to identify relationships. An example of LSA: Using a search engine search for “sand”. Documents are returned which do not contain the s
Probabilistic latent semantic analysis - Wikipedia
Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic
probabilistic latent semantic analysis - 機械学習の「朱鷺の杜Wiki」
probabilistic latent semantic analysis (pLSA)† 文書と単語など，離散2変数の計数データの生成モデル． 文書(document)：\(d\in\mathcal{D}=\{d_1,\ldots,d_N\}\)， 語(word)：\(w\in\mathcal{W}=\{w_1,\ldots,w_M\}\)， 潜在変数の話題(topic)：\(z\in\mathcal{Z}=\{z_1,\ldots,z_K\}\) を使った文書と単語の生成モデルがpLSA (probabilistic latent semantic analysis) \[\Pr[d,w]=\Pr[d]\sum_{z\in\mathcal{Z}}\Pr[w|z]\Pr[z|d]\] これは，文書と語について対称に定義することもできる \[\Pr[d,w]=\sum_{z\in\mat
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A demonstration of bag-of-words classifiers
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