Why direct LDA is not equivalent to LDA

Why direct LDA is not equivalent to LDA

In this paper, we present counterarguments against the direct LDA algorithm (D-LDA), which was previously claimed to be equivalent to Linear Discriminant Analysis (LDA). We show from Bayesian decision theory that D-LDA is actually a special case of LDA by directly taking the linear space of class means as the LDA solution. The pooled covariance estimate is completely ignored. Furthermore, we de...

Equivalence Between LDA/QR and Direct LDA

Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem. This paper establishes the equivalent relationship between LDA/QR and DLDA. They ca...

Rethinking LDA: Why Priors Matter

Implementations of topic models typically use symmetric Dirichlet priors with fixed concentration parameters, with the implicit assumption that such “smoothing parameters” have little practical effect. In this paper, we explore several classes of structured priors for topic models. We find that an asymmetric Dirichlet prior over the document–topic distributions has substantial advantages over a...

Sentic LDA

The advent of the Social Web has provided netizens with new tools for creating and sharing, in a timeand costefficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In...

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