In this paper, we propose a method to raise the accuracy of text classification based on latent topics, reconsidering the techniques necessary for good classification – for example, to decide important sentences in a document, the sentences with important words are usually regarded as important sentences. In this case, tf.idf is often used to decide important words. On the other hand, we apply ...

Recently, deep learning based clustering methods are shown superior to traditional ones by jointly conducting representation learning and clustering. These methods rely on the assumptions that the number of clusters is known, and that there is one single partition over the data and all attributes define that partition. However, in real-world applications, prior knowledge of the number of cluste...

Ghahramani and colleagues have proposed an interesting class of infinite latent feature (ILF) models. The basic premise of ILF models is that there are infinitely many latent predictors represented in the population, with any particular subject having a finite selection. This is presented as an important advance over models that allow a finite number of latent variables. ILF models are most use...

Abstract. Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. ...

For swine dysentery, which is caused by Brachyspira hyodysenteriae infection and is an economically important disease in intensive pig production systems worldwide, a perfect or error-free diagnostic test ("gold standard") is not available. In the absence of a gold standard, Bayesian latent class modelling is a well-established methodology for robust diagnostic test evaluation. In contrast to r...

Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement mo...

Over the past several years more significant books have been published on latent class and other types of finite mixture models than any other class of statistical models. The recent increase in interest in latent class models is due to the development of extended computer algorithms, which allow today's computers to perform latent class analysis on data containing more than just a few variable...

The latent class (LC) models that have been developed so far assume that observations are independent. Parametric and nonparametric random-coefficient LC models are proposed here, which will make it possible to modify this assumption. For example, the models can be used for the analysis of data collected with complex sampling designs, data with a multilevel structure, and multiple-group data fo...