• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.15-22
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• 2011

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1093-1102
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• 2010

Principal component analysis (PCA) is a well-known method for dimension reduction while maintaining most of the variation in data. Although PCA has been applied to many areas successfully, it is sensitive to outliers. Several variants of PCA have been proposed to resolve the problem and, among the variants, robust fuzzy PCA (RF-PCA) demonstrated promising results. RF-PCA uses fuzzy memberships to reduce the noise sensitivity. However, there are also problems in RF-PCA and the convergence property is one of them. RF-PCA uses two different objective functions to update memberships and principal components, which is the main reason of the lack of convergence property. The difference between two functions also slows the convergence and deteriorates the solutions of RF-PCA. In this paper, a variant of RF-PCA, called RF-PCA2, is proposed. RF-PCA2 uses an integrated objective function both for memberships and principal components. By using alternating optimization, RF-PCA2 is guaranteed to converge on a local optimum. Furthermore, RF-PCA2 converges faster than RF-PCA and the solutions found are more similar to the desired solutions than those of RF-PCA. Experimental results also support this.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.25-31
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• 2011

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction. Although PCA has been applied in many areas successfully, it is sensitive to outliers due to the use of sum-square-error. Several variants of PCA have been proposed to resolve the noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA2, however, still can fall into a local optimum due to equal initial membership values for all data points. Another reason comes from the fact that RF-PCA2 is based on sum-square-error although fuzzy memberships are incorporated. In this paper, a variant of RF-PCA2 called RF-PCA3 is proposed. The proposed algorithm is based on the objective function of RF-PCA2. RF-PCA3 augments RF-PCA2 with the objective function of PCA and initial membership calculation using data distribution, which make RF-PCA3 to have more chance to converge on a better solution than that of RF-PCA2. RF-PCA3 outperforms RF-PCA2, which is demonstrated by experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.3
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• pp.249-254
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• 2005

In this paper, we analysis the recognition performance of PCA/LDA by distance measures. We are adapt to ORL face database with the fourteen distance measures. In case of PCA, it has high performance for the manhattan distance and the weighted SSE distance to face recognition, In case of PCA/LDA, it has high performance for the angle-based distance and the modified SSE distance. Also, PCA/LDA is better than PCA for reduction of dimension. Therefore, the PCA/LDA method and the angle-based distance have the most performance and a few dimension for face recognition with ORL face database.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.784-794
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• 2023

Objective: To determine whether dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) can be used to evaluate posterior cerebral circulation in pediatric patients with moyamoya disease (MMD) who underwent anterior revascularization. Materials and Methods: This study retrospectively included 73 patients with MMD who underwent DSC perfusion MRI (age, 12.2 ± 6.1 years) between January 2016 and December 2020, owing to recent-onset clinical symptoms during the follow-up period after completion of anterior revascularization. DSC perfusion images were analyzed using a dedicated software package (NordicICE; Nordic NeuroLab) for the middle cerebral artery (MCA), posterior cerebral artery (PCA), and posterior border zone between the two regions (PCA-MCA). Patients were divided into two groups; the PCA stenosis group included 30 patients with newly confirmed PCA involvement, while the no PCA stenosis group included 43 patients without PCA involvement. The relationship between DSC perfusion parameters and PCA stenosis, as well as the performance of the parameters in discriminating between groups, were analyzed. Results: In the PCA stenosis group, the mean follow-up duration was 5.3 years after anterior revascularization, and visual disturbances were a common symptom. Normalized cerebral blood volume was increased, and both the normalized time-topeak (nTTP) and mean transit time values were significantly delayed in the PCA stenosis group compared with those in the no PCA stenosis group in the PCA and PCA-MCA border zones. TTP_PCA (odds ratio [OR] = 6.745; 95% confidence interval [CI] = 2.665-17.074; P < 0.001) and CBV_PCA-MCA (OR = 1.567; 95% CI = 1.021-2.406; P = 0.040) were independently associated with PCA stenosis. TTP_PCA showed the highest receiver operating characteristic curve area in discriminating for PCA stenosis (0.895; 95% CI = 0.803-0.986). Conclusion: nTTP can be used to effectively diagnose PCA stenosis. Therefore, DSC perfusion MRI may be a valuable tool for monitoring PCA stenosis in patients with MMD.

• MALSORI
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• no.66
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• The Korean Journal of Pain
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• v.10 no.1
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• pp.34-41
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• 1997

Background : Patient-controlled analgesia(PCA) is a safe and effective technique for providing postoperative pain relief. Studies that compare epidural vs intravenous routes of opiate administration show conflicting results. We designed a prospective, randomized, controlled study to evaluate the safety and efficacy of epidural(EPI-PCA) morphine-bupivacaine versus intravenous (IV-PCA) nalbuphine when administered with a PCA system. Methods : Forty healthy women were randomly assigned to receive an epidural bolus of morphine 3 mg and 0.5% bupivacaine 10 ml, followed by a EPI-PCA with 0.01% morphine and 0.143% bupivacane (basal infusion 1 ml/hr, bolus 1 ml, lock-out interval 30 min) or intravenous bolus of nalbuphine 0.1 mg/kg followed by a IV-PCA with nalbuphine(basal infusion 1 mg/hr, bolus 1 ml, lock-out interval 20 min) for pain relief after cesarean delivery. This study was conducted for 2 days after cesarean section to compare the analgesic efficacy, side effects, patient satisfaction either as EPI-PCA or as IV-PCA. Results : EPI-PCA group had significant lower visual analog pain scale(VAS) at immediate postoperative period, whereas no significant difference was observed when pain was assessed at other time sequence. Urinary retention and pruritus were more frequent with EPI-PCA group, although the incidence of other side effects were the same. Conclusions : Although EPI-PCA with morphine-bupivacaine was of significantly lower VAS at immediate postoperative period, IV-PCA with nalbuphine is a safe and effective alternative to EPI-PCA with morphine-bupivacaine for providing pain relief after cesarean delivery. Further studies about IV-PCA with nalbuphine are needed to control the immediate postoperative pain and to further improve effective pain management.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.538-540
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• 2001

PCA는 다변수 데이터 해석법 중 가장 널리 알려진 방법 중 하나로 많은 응용을 가지고 있다. 그런데, PCA는 선형 모델이어서 비선형 구조를 분석하는데 효과적이지 않다. 이를 극복하기 위해서 PCA의 조합을 이용하는 PCA 혼합 모형이 제안되었다. PCA 혼합 모형의 핵심은 구조 선택, 즉 mixture 요소의 수와 PCA 기저의 수의 결정 인데 그의 체계적인 결정 방법이 필요하다. 본 논문에서는 단순화된 PCA 혼합 모형과 이를 위한 효율적인 구조 선택 방법을 제안한다. 각각의 mixture 요소 수에 대해서 모든 PCA 기저를 갖도록 한 상태에서 PCA 혼합 모형의 파라미터를 EM 알고리즘을 써서 결정한다. 최적의 mixture 요소의 수는 오류를 최소로 하는 것으로 결정한다. PCA 기저의 수는 PCA의 정렬성 특성을 이용해서 중요도가 적은 기저부터 하나씩 잘라 내며 오류가 최소로 하는 것으로 결정한다. 제안된 방법은 특히 다차원 데이터의 경우에 EM 학습의 횟수를 많이 줄인다. 인공 데이터에 대한 실험은 제안된 방법이 적절한 모델 구조를 결정한다는 것을 보여준다. 또, 눈 감지에 대한 실험은 제안된 방법이 실용적으로도 유용하다는 것을 보여준다.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.14 no.1
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• pp.18-28
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• 2007

Purpose: This study was to identify knowledge, attitude, use and state of the Patient Controlled Analgesics (PCA) in postoperative patients. Method: The research design was a descriptive research. From December 7, 2005 to January 6, 2006, 102 postoperative patients in a university hospital at Daegu were participated in the study Results: Analgesics with PCA were mainly morphine complex 73.5% and Demerol complex 26.5%. Previous experience of using PCA was only 28.4%, and the main sources of information were other post-op patients and families(43.1%). The most common reason of choice was a recommendation from other post-op patients and families(46.1%). The most common side effects of PCA were nausea and vomiting(20.6%). About 57% of the patients were satisfied with PCA, and pain scores decreased with PCA. Mean score for knowledge about PCA was 2.55 out of a possible 6, and for attitude related to pain medication. 2.31 out of possible 5. Conclusion: To increase the score on knowledge of PCA, a structured preoperative PCA education program should be developed by nursing staff.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.4
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• pp.291-295
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• 2005

In this paper, we propose a face recognition method using modified 2-D PCA. While the previous PCA method computes the covariance matrix by using one dimensional vectors, the 2-D PCA method computes the covariance matrix by directly using direct two dimensional image, and extracts the feature vectors by solving eigenvalue problem. The proposed method recognizes the faces by applying the modified 2-D PCA to face images and it gets linear transformation matrix using two covariance matrices. The experimental results indicates that the proposed method improved about $1\%$ and achieved more stability in recognition rate than conventional 2-D PCA.