Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, ...

In this paper,we introduce a new topic model named Gaussian-LDA, which is more suitable to model continuous data. Topic Model based on latent Dirichlet allocation (LDA) is widely used for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a multinomial distribution over ...

The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted...

Classification methods such as linear discriminant analysis (LDA) have been widely applied to fault detection in industrial processes. In this case, the problem consists of classifying the operation as normal or faulty on the basis of monitored variables. If the number of such variables is large, selection techniques may be used to choose an informative subset of features in order to obtain a c...

Abstract Web API is a popular way to organize network services in cloud computing environment. However, it challenge find an appropriate service for the requestor from massive services. Service clustering can improve efficiency of discovery its ability reducing search space. Latent Dirichlet Allocation (LDA) most frequently used topic model clustering. To further representation LDA, we propose ...

A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical Fisher linear discriminant analysis (LDA) and useful, for example, in supervised segmentation tasks in which high-dimensional feature vector describes the local structure of the image. In general, the main idea of t...

Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotate...

Neutron diffraction with isotope substitution is used to determine the structures of high (HDA) and low (LDA) density amorphous ice. Both "phases" are fully hydrogen bonded, tetrahedral networks, with local order similarities between LDA and ice Ih, and HDA and liquid water. Moving from HDA, through liquid water and LDA to ice Ih, the second shell radial order increases at the expense of spatia...

In this paper, a combination methodology of Discrete Cosine Transform (DCT) and an improved D-LDA and Neural Networks was proposed. DCT can compress the information of original signal efficiently, so we reduce the dimension firstly and then extract features by improved D-LDA on the low dimension space to overcome the shortages of LDA maximally. After calculating the eigenvectors and a new Fishe...

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion that benefits handling the uncertainty near the decision boundary by means of a fuzzy linear programming approach with fuzzy resources. The method proposed has low computational complexity because of its linear character...