A Partition Metric for Clustering Features Analysis

A new distance function to compare arbitrary partitions is proposed. Clustering of image collections and image segmentation give objects to be matched. Offered metric intends for combination of visual features and metadata analysis to solve a semantic gap between low-level visual features and high-level human concept.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

A Statistical Approach for Multilingual Document Clustering and Topic Extraction from Clusters

2000 Mathematics Subject Classification: 62H30

Low Volatility Options and Numerical Diffusion of Finite Difference Schemes

2000 Mathematics Subject Classification: 65M06, 65M12.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Optimal investment under stochastic volatility and power type utility function

2000 Mathematics Subject Classification: 37F21, 70H20, 37L40, 37C40, 91G80, 93E20.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.

Volt-e likviditási válság? Volatilitás és likviditás kapcsolatának vizsgálata (Was there a liquidity crisis? Analysis the relationship between volatility and liquidity)

Széleskörűen alátámasztott, empirikus tény, hogy önmagában a nagyobb volatilitás csökkenti a piac likviditását, vagyis változékonyabb piacokon várhatóan nagyobb lesz egy-egy tranzakció áreltérítő hatása. Kutatásomban azt a kérdést vizsgáltam, hogy a Budapesti Értéktőzsdén az OTP-részvény piacán a 2007/2008-as válságban tapasztalható, átmeneti likviditáscsökkenés betudható volt-e egyszerűen a megnövekedett volatilitásnak, vagy ezen túl abban más tényezők (pl. a szereplők körének és viselkedésének drasztikus megváltozása, általános forráscsökkenés stb.) is szerepet játszhattak-e. A volatilitást a loghozamok szórásával, illetve a tényleges ársávval, míg az illikviditást a Budapesti Likviditási Mértékkel (BLM) reprezentáltam. Egyrészt azt állapítottam meg, hogy az OTP esetében a tényleges ársáv szorosabban korrelál a BLM-mel, mint a szórás. Másrészt az is egyértelmű, hogy a válság előtti kapcsolat a volatilitás és a likviditás között a válságban és azután már jelentősen megváltozott. Válságban az illikviditás jóval nagyobb volt, mint amit a volatilitás növekedése alapján vártunk, a válság lecsengése után azonban megfordult ez a reláció. _________ It is a widely supported empirical fact, that the greater volatility in itself decreases the liquidity of the market, namely more volatile a market is, the higher a transaction’s price impact will be. I have examined in my paper the question, whether the decrease of liquidity during the crisis of 2007/2008 in case of the OTP stock – traded on the Budapest Stock Exchange – was the consequence of the increased volatility, or other factors had an effect on the illiquidity as well (e.g.: the drastic change of market participants’ behaviour; reduction of fi nancing sources; etc.). I have represented volatility with the standard deviation of the logreturns, and with the true range, while the illiquidity with the Budapest Liquidity Measure (BLM). On one hand I have identifi ed, that in case of the OTP, the true range has a stronger relationship with the BLM than the standard deviation has. On the other hand it was clear, that the relationship between volatility and liquidity has changed notably during and after the crisis. During crisis the illiquidity was greater than what I have estimated based on the volatility increase, but after the crisis this relation has changed.

An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

The Food Service Industry Environment: Market Volatility Analysis

In their dialogue entitled - The Food Service Industry Environment: Market Volatility Analysis - by Alex F. De Noble, Assistant Professor of Management, San Diego State University and Michael D. Olsen, Associate Professor and Director, Division of Hotel, Restaurant & Institutional Management at Virginia Polytechnic Institute and State University, De Noble and Olson preface the discussion by saying: “Hospitality executives, as a whole, do not believe they exist in a volatile environment and spend little time or effort in assessing how current and future activity in the environment will affect their success or failure. The authors highlight potential differences that may exist between executives' perceptions and objective indicators of environmental volatility within the hospitality industry and suggest that executives change these perceptions by incorporating the assumption of a much more dynamic environment into their future strategic planning efforts. Objective, empirical evidence of the dynamic nature of the hospitality environment is presented and compared to several studies pertaining to environmental perceptions of the industry.” That weighty thesis statement presumes that hospitality executives/managers do not fully comprehend the environment in which they operate. The authors provide a contrast, which conventional wisdom would seem to support and satisfy. “Broadly speaking, the operating environment of an organization is represented by its task domain,” say the authors. “This task domain consists of such elements as a firm's customers, suppliers, competitors, and regulatory groups.” These are dynamic actors and the underpinnings of change, say the authors by way of citation. “The most difficult aspect for management in this regard tends to be the development of a proper definition of the environment of their particular firm. Being able to precisely define who the customers, competitors, suppliers, and regulatory groups are within the environment of the firm is no easy task, yet is imperative if proper planning is to occur,” De Noble and Olson further contribute to support their thesis statement. The article is bloated, and that’s not necessarily a bad thing, with tables both survey and empirically driven, to illustrate market volatility. One such table is the Bates and Eldredge outline; Table-6 in the article. “This comprehensive outline…should prove to be useful to most executives in expanding their perception of the environment of their firm,” say De Noble and Olson. “It is, however, only a suggested outline,” they advise. “…risk should be incorporated into every investment decision, especially in a volatile environment,” say the authors. De Noble and Olson close with an intriguing formula to gauge volatility in an environment.

The Study of Clustering of Taiwanese Tourists' Motivations to Hong Kong

Abstract Driven by the political and economic forces of cross-strait, Taiwan has become one of the major source markets for Hong Kong tourism industry since 1987. The major purposes of this study were to investigate the following factors (1) The influential factors of travel motivation, (2) The clusters of travel motivations, (3) The marketing segmentation of clusters of Taiwanese tourists to visit Hong Kong. Through ten travel agents, self-report surveys were distributed to collect data from 366 Taiwanese travelers. Hence, four push factors and six pull factors were identified as travel motivations through the factor analysis. Combined with the cluster analysis; five new groups were founded. Finally, five clusters which process unique profiles (location difference, visiting frequency, travel satisfaction, and destination loyalty) were addressed. The suggestions of developing effective market strategies to attract Taiwanese tourists to Hong Kong were also provided.

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positivedefiniteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.