• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.7
• /
• pp.1475-1479
• /
• 2004

High dimensional data requires efficient feature extraction techliques. Though PCA(Principal Component Analysis) is a famous feature extraction method it requires huge memory space and computational cost is high. In this paper we use incremental PCA for feature extraction on high dimensional data. Through experiment we show that proposed method is superior to APEX model.

• The KIPS Transactions:PartB
• /
• v.16B no.1
• /
• pp.63-70
• /
• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.4
• /
• pp.320-325
• /
• 2010

This paper is about the method and procedure to register the candidate sequentially in the face recognition system using the PCA(Principal Components Analysis). We use the method to update the principal components sequentially with the SKL algorithm which is improved R-SVD algorithm. This algorithm enable us to solve the re-training problem of the increase the candidates number sequentially in the face recognition using the PCA. Also this algorithm can use in robust tracking system with the bright change based to the principal components. This paper proposes the procedure in the face recognition system which sequentially updates the principal components using the SKL algorithm. Then we compared the face recognition performance with the batch procedure for calculating the principal components using the standard KL algorithm and confirms the effects of the forgetting factor in the SKL algorithm experimentally.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.243-248
• /
• 2003

An incremental kernel principal component analysis (IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis (KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the feature extraction and classification problem on nonlinear data set.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.11a
• /
• pp.733-736
• /
• 2007

최근 생명 공학 기술의 발달로 마이크로 단위의 실험이 가능해지고 하나의 칩상에 수 만개의 유전자들의 발현 양상을 보다 쉽게 관찰할 수 있게 되었다. DNA 칩 기술에 의해 얻어지는 마이크로어레이(microarray) 데이터는 세포나 조직 내의 유전자 발현도(expression level)를 측정한 것으로 질병 진단이나 유전자 기능 예측 등에 이용되고 있다. 본 논문에서는 대량의 시계열 마이크로어레이 데이터 분석을 위해 효율적으로 데이터의 차원을 판단하는 점진적 주성분 분석을 이용하여 데이터의 차원을 축소 한다. 제안된 방법은 실제 시계열 마이크로어레이 데이터인 yeast cell cycle 데이터에 적용되었고, 데이터 차원 축소에 대한 효율성을 검증하기 위해 클러스터링을 수행하였다. 그 결과 데이터를 축소하여 클러스터링을 수행한 경우 학습 성능이 향상 된 결과를 보였다.

• Journal of KIISE:Software and Applications
• /
• v.31 no.3
• /
• pp.361-368
• /
• 2004

The purpose of this study is to propose a new on-line nonlinear PCA(OL-NPCA) method for a nonlinear feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. First, applying KPCA to N patterns requires storing and finding the eigenvectors of a N${\times}$N kernel matrix, which is infeasible for a large number of data N. Second problem is that in order to update the eigenvectors with an another data, the whole eigenspace should be recomputed. OL-NPCA overcomes these problems by incremental eigenspace update method with a feature mapping function. According to the experimental results, which comes from applying OL-NPCA to a toy and a large data problem, OL-NPCA shows following advantages. First, OL-NPCA is more efficient in memory requirement than KPCA. Second advantage is that OL-NPCA is comparable in performance to KPCA. Furthermore, performance of OL-NPCA can be easily improved by re-learning the data.

• Spatial Information Research
• /
• v.20 no.4
• /
• pp.17-27
• /
• 2012

This study was performed to evaluate the spatial distribution of heavy metals using principal component analysis and Ordinary Kriging technique in the Gangwon Mine site. In the soils from the sub soil, the contents of Zn and Ni in the PC1 were gradually dispersed from south to north direction, while the components of Cd and Hg in the PC2 showed an increase significantly from middle-south area in the Gangwon Mine site. According to the cluster analysis, pollutant metals of As and Cu were presented a strong spatial autocorrelation structure in cluster D. The concentration of As was 0.83mg/kg and shown to increase from the south to north direction. The spatial distribution maps of the soil components using geostatistical method might be important in future soil remediation studies and help decision-makers assess the potential health risk affects of the abandoned mining sites.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.1
• /
• pp.56-63
• /
• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.687-699
• /
• 2020

The government-led top-down development approach for weapons system faces the problem of technological obsolescence now that technology has rapidly grown. As a result, the government has gradually expanded the corporate-led bottom-up project implementation method to the defense industry. The key success factor of the bottom-up project implementation is the ability of defense companies to plan their technologies. This paper presented a method of analyzing patent data through data mining technique so that domestic defense companies can utilize it for technology planning activities. The main content is to propose corporate selection techniques corresponding to the defense communication-electronics sectors and conduct principal component analysis and cluster analysis for the International Patent Classification. Through this, the technology was classified into four groups based on the patents of nine companies and the representative enterprises of each group were derived.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.27 no.2
• /
• pp.200-214
• /
• 1994

In order to clarify the benthic environmental properties as a part of a study on the macrobenthic community in the Chinhae Bay System, water temperature, salinity and dissolved oxygen (DO) in surface and bottom water layers, mean grain size (${\phi}$) and sediment organic carborn (SOC) in surface sediment were analyzed at twelve stations during the period from June 1987 to May 1990. A high sediment organic carbon and hypoxic condition in bottom water due to the development of summer stratification and fine sediment texture toward the inner bay were important environmental characteristics of Chinhae Bay. Hypoxic conditions began to develop in the inner bay from May, and gradually spread toward the outer bay in summer with a peak in September when half the bay was affected by this oxygen deficiency. Recovery from this hypoxic condition in the bottom layer was observed from the beginning of autumn together with a disappearance of the summer stratification. Principal component analyses were carried out from the following five environmental variables:mean water temperature, salinity, dissolved oxygen in the bottom layer and mean grain size, sediment organic carbon in surface sediment. The twelve stations were classified into four areal groups based on the analyses. The division of the areal groups had high correlations to the sediment organic carbon content.