The potential energy landscape (PEL) formalism is a valuable approach within statistical mechanics to describe supercooled liquids and glasses. Here we use the PEL formalism and computer simulations to study the pressure-induced transformations between low-density amorphous ice (LDA) and high-density amorphous ice (HDA) at different temperatures. We employ the ST2 water model for which the LDA-...

In this paper, a relationship between Linear Discriminant Analysis (LDA) and the generalized Minimum Squared Error (MSE) solution is presented. The generalized MSE solution is shown to be equivalent to applying a certain classification rule in the space defined by LDA. The relationship between the MSE solution and Fisher Discriminant Analysis (FDA) is extended to multi-class problems and also t...

Several reactions mediated by lithium diisopropylamide (LDA) with added hexamethylphosphoramide (HMPA) are described. The N-isopropylimine of cyclohexanone lithiates via an ensemble of monomer-based pathways. Conjugate addition of LDA/HMPA to an unsaturated ester proceeds via di- and tetra-HMPA-solvated dimers. Deprotonation of norbornene epoxide by LDA/HMPA proceeds via an intermediate metalat...

Face recognition has great demands and become one of the most important research area of pattern recognition but there are several issues involved in it. Unsupervised statistical methods i.e. PCA, LDA, ICA are the most popular algorithms in face recognition that finds the set of basis images and represents faces as linear combination of those images. This paper presents a novel layered face rec...

0167-8655/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.patrec.2010.09.025 q The work of O. Kursun was supported by Scienti nation Unit of Istanbul University under the grant YA ⇑ Corresponding author. Tel.: +90 212 473 7070/17 E-mail addresses: [email protected] (O. Kurs Alpaydin), [email protected] (O.V. Favorov). Fisher’s linear discriminant analysis (LDA) is one of the most ...

Latent Dirichlet allocation (LDA) is an increasingly popular tool for data analysis in many domains. If LDA output affects decision making (especially when money is involved), there is an incentive for attackers to compromise it. We ask the question: how can an attacker minimally poison the corpus so that LDA produces topics that the attacker wants the LDA user to see? Answering this question i...

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference of LDA. In this paper, we propose a technique called MapReduce LDA (Mr. LDA) to accommodate very large corpus collections in the MapReduce framework. In contrast to other techni...

To improve the performance of Linear Discriminant Analysis (LDA) for early detection of diseases using Electronic Health Records (EHR) data, we propose ED – a novel framework for EHR based Early Detection of Diseases on top of Covariance-Regularized LDA models. Specifically, ED employs a non-sparse inverse covariance matrix (or namely precision matrix) estimator derived from graphical lasso and...

A New Approach to Automatic Summarization by Using Latent Dirichlet Allocation in Conditional Random Field Xiaofeng Wu, Chengqing Zong (National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China) Abustract: In recent years, Latent Dirichlet Allocation(LDA) has been used more and more in Document Clustering, Classification, Segmentation, and some one has used it in ...

In this paper, we propose a novel problem of summarizing textual corporate risk factor disclosure, which aims to simultaneously infer the risk types across corpus and assign each risk factor to its most probable risk type. To solve the problem, we develop a variation of LDA topic model called Sent-LDA. The variational EM learning algorithm, which guarantees fast convergence, is derived and impl...