• Communications for Statistical Applications and Methods
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• 제28권3호
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• pp.267-279
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• 2021

A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• Membrane and Water Treatment
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• 제2권2호
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• pp.105-119
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• 2011

This study presents the tests of an Immersed Membrane BioReactor (IMBR) equipped with a draft tube and focuses on the influence of hydrodynamic conditions on membrane fouling in a pilot-scale using a hollow fiber membrane module of ZW-10 under ambient conditions. In this system, the cross-flow velocities across the membrane surface were induced by a cylindrical draft-tube. The relationship between cross-flow velocity and aeration strength and the influence of the cross-flow on fouling rate (under various hydrodynamic conditions) were investigated using Multi-Dimension Scaling (MDS) analysis. MDS technique is especially suitable for samples with many variables and has relatively few observations, as the data about Membrane Bio-Reactor (MBR) often is. Observations and variables are analyzed simultaneously. According to the results, a specialized form of MDS, CoPlot enables presentation of the results in a two dimensional space and when plotting variables ratio (output/input) rather than original data the efficient units can be visualized clearly. The results indicate that: (i) aeration plays an important role in IMBR performance; (ii) implementing the MDS approach with reference to the variables ratio is consequently useful to characterize performance changes for data classification.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2015년도 춘계 학술논문 발표대회
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• pp.240-241
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• 2015

As buildings and especially eco-friendly facilities are increasing, maintenance of the aged buildings are interested by many maintenance companies. Therefore, a lot of companies are increasing by the maintenance form. These Earned Value should try to minimize the increasing forms. Therefore, this study has an effort to gather data related to earned value of building facility maintenance and analyze the data in terms of local dimension, used duration dimension, building types dimension and so on in order to prevent the building deterioration.

• 복식문화연구
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• 제11권2호
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• pp.219-230
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• 2003

The purpose of this study was to analyze the structure of consumption emotions that consumers experienced in the process of consuming apparel products. Data was collected from 144 female college students living in Busan, and analyzed by salience, diversity, H-index, Clamor's V, and multi-dimensional scaling. The results showed as following; 1. The consumption emotions related to apparel products appeared three dimensions; ‘Relaxed-tense’ dimension, ‘Pleasant-unpleasant’ dimension, and ‘Outward-inward’ dimension. Considering elements of consumption system, the dimensions of consumption emotions in relation to apparel performances were 'Pleasant-unpleasant' and ‘Outward-inward’. The dimensions of consumption emotions experienced in usage situations were ‘Relaxed-tense’ and ‘pleasant-unpleasant’. The consumption emotions related to specific products were composed of ‘Pleasant-unpleasant’ dimension and ‘Outward-inward’ dimension. 2. As the multi-dimension map of this study has much space, it suggested that the scope of consumption emotions related to apparel products was more limited than those related to general situations and products. 3. The structure of consumption emotions in relation to apparel performances appeared to be bisected, while those related to usage situations showed relatively to be dispersed. 4. Although Pleasant-unpleasant dimension was consistent with results of prestudies, the dimensions of ‘Relaxed-tense’ and ‘Outward-inward’ were newly confirmed as the dimensions of consumption emotions related to apparel products. Therefore, consumer's consumption emotions of apparel products were composed of three dimensions, tended to be more limited than those of general consumption situations and products, and differentiated across apparel performances, usage situation, and specific products.

• 융합신호처리학회논문지
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• 제7권3호
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• pp.116-121
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• 2006

본 논문에서는 다양한 잡음환경에서의 실시간 적응화 기법을 적용하기 위한 선결 과제로 다차원 음성 특정 벡터를 저차원으로 축소하는 방법을 제안한다. 제안된 방법은 특징 벡터를 확률 우도 값으로 매핑시켜 비선형적으로 축소하는 방법으로 음성 / 비음성의 분류는 우도비 검증 (Likelihood Ratio Test; LRT) 을 이용하여 분류하였다. 실험 결과 고차원 특징 벡터를 이용하여 분류한 결과와 대등하게 분류됨을 확인할 수 있었다. 그리고, 제안된 방법에 의해 검출된 음성 데이터를 이용한 음성인식 실험에서도 10차 MFCC(Mel-Frequency Cepstral Coefficient)를 사용하여 분류한 경우와 대등한 인식률을 보여주었다.

• 경영과학
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• 제24권2호
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• pp.57-72
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• 2007

Many Applications of scientific research have coupled with functional data signal clustering techniques to discover novel characteristics that can be used for the diagnoses of several issues. In this article we present an interpretable multi-scale cluster hierarchy framework for clustering functional data using its multi-aspect frequency information. The suggested method focuses on how to effectively select transformed features/variables in unsupervised manner so that finally reduce the data dimension and achieve the multi-purposed clustering. Specially, we apply our suggested method to mutual fund returns and make superior-performing funds group based on different aspects such as global patterns, seasonal variations, levels of noise, and their combinations. To promise our method producing a quality cluster hierarchy, we give some empirical results under the simulation study and a set of real life data. This research will contribute to financial market analysis and flexibly fit to other research fields with clustering purposes.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.

• Journal of Information Technology Applications and Management
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• 제17권3호
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• pp.135-150
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• 2010

Interactivity has been referred to as an important element promoting students' active participation in virtual classes. Assuming that interactivity cannot be defined by a single dimension, this study proposes multi-dimensional interactivity. Multi-dimensional interactivity includes all types of interactivity in e-learning. This study explored multi-dimensional interactivity which affects learners' satisfaction with e-learning. Data were collected from 132 students who had attended e-learning courses and the relationship between multi-dimensional interactivity and learners' satisfaction levels were tested through regression analysis. The result of this study showed that mechanical, reactive, and creative interactivity were positively related to learners' satisfaction. However, social interactivity seemed not to be related to learners' satisfaction. This study provides new insights on interactivity and verifies the importance of the multi-dimensional interactivity. The result of this study is expected to provide practical implications for interactivity strategies in e-learning.