In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improvements in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the E...

Latent Dirichlet Allocation (LDA) is an unsupervised, statistical approach to document modeling that discovers latent semantic topics in large collections of text documents. LDA posits that words carry strong semantic information, and documents discussing similar topics will use a similar group of words. Latent topics are thus discovered by identifying groups of words in the corpus that frequen...

Linear discriminant analysis (LDA) has played an important role for dimension reduction in patter recognition field. Basically, LDA has three deficiencies in dealing with classification problems. First, LDA is well-suited only for normally distributed data. Second, the number of features can be extracted are limited by the rank of between-class scatter matrix. Third, the singularity problem ari...

Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply the LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make the LDA ap...

BACKGROUND More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA) and its modification methods for the classification of cancer based on gene expression data. METHODS The clas...

Model based deep neural network (DNN) adaptation approaches often require multi-pass decoding in test time. Input feature based DNN adaptation, for example, based on latent Dirichlet allocation (LDA) clustering, provide a more efficient alternative. In conventional LDA clustering, the transition and correlation between neighboring clusters is ignored. In order to address this issue, a recurrent...

In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improvements in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the E...

Latent Dirichlet allocation (LDA) is a topic model that has been applied to various fields, including user profiling and event summarization on Twitter. When LDA is applied to tweet collections, it generally treats all aggregated tweets of a user as a single document. Twitter-LDA, which assumes a single tweet consists of a single topic, has been proposed and has shown that it is superior in top...

In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features....

We examined whether the postoperative prognosis of beef cattle with left displaced abomasum (LDA) can be estimated from changes in laboratory parameters. Preoperatively, beef cattle with LDA showed increases in plasma glucose with decreased serum insulin in the glucose tolerance test compared to non-LDA cattle. Postoperatively, the cattle with LDA were retrospectively divided into two groups, g...