For two-class discrimination, Ref. [1] claimed that, when covariance matrices of the two classes were unequal, a (class) unbalanced dataset had a negative effect on the performance of linear discriminant analysis (LDA). Through re-balancing 10 realworld datasets, Ref. [1] provided empirical evidence to support the claim using AUC (Area Under the receiver operating characteristic Curve) as the p...

This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as “topic models” to produce a lowdimensional representation of documents, while Probabilistic Context-Free Grammars (PCFGs) define distributions over trees. The paper begins by showing that LDA topic models can be viewed as a special kind of...

The hidden topic model of Chinese text, which possesses complicated semantics, is urgently needed, since China has occupied an increasingly significant role during the booming development of globalization over recent years. This paper details and elaborates the basic process of extracting latent Chinese topics by demonstrating a Chinese topic extraction schema based on Latent Dirichlet Allocati...

In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes. Traditional LDA sets assumptions related to Gaussian class distribution and neglects influence of outlier classes, that might hurt in performance. We exploit intuitions coming from a probabilistic inte...

Loss Differentiation Algorithms (LDA) are currently used to determine the cause of packet losses with an aim of improving TCP performance over wireless networks. In this work, we propose a cross-layer solution based on two LDA in order to classify the loss origin on an 802.11 link and then to react consequently. The first LDA scheme, acting at the MAC layer, allows differentiating losses due to...

Electronic documents on the Internet are always generated with many kinds of side information. Although those massive kinds of information make the analysis become very difficult, models would fit and analyze data well if they could make full use of those kinds of side information. This paper, base on the study on probabilistic topic model, proposes a new improved LDA model which is suitable fo...

While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the cl...

Latent Dirichlet allocation (LDA) has been successful for document modeling. LDA extracts the latent topics across documents. Words in a document are generated by the same topic distribution. However, in real-world documents, the usage of words in different paragraphs is varied and accompanied with different writing styles. This study extends the LDA and copes with the variations of topic infor...

Purpose: To develop computer-aided diagnosis (CADx) models using both mammographic andsonographic descriptors and to estimate the generalization performance of these models onfuture cases.Materials and Methods: Institutional Review Board approval was obtained for this HIPPA-compliant study. Mammographic and sonographic exams were performed on 737 patients,yielding 803 br...

Recently, some statistical topic modeling approaches have been widely applied in the field of supervised document classification. However, there are few researches on these under label noise, which exists real-world applications. For example, many large-scale datasets collected from websites or annotated by varying quality human-workers, and then a mislabeled items. In this paper, we propose tw...