Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
ÇѱÛÁ¦¸ñ(Korean Title) |
ÀâÀ½ ¹Î°¨¼ºÀÌ °³¼±µÈ ÆÛÁö ÁÖ¼ººÐ ºÐ¼® |
¿µ¹®Á¦¸ñ(English Title) |
An Improved Robust Fuzzy Principal Component Analysis |
ÀúÀÚ(Author) |
Çã°æ¿ë
¿ì¿µ¿î
±è¼ºÈÆ
Gyeongyong Heo
Young Woon Woo
Seong Hoon Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 14 NO. 05 PP. 1093 ~ 1102 (2010. 05) |
Çѱ۳»¿ë (Korean Abstract) |
ÁÖ¼ººÐ ºÐ¼®(PCA)Àº µ¥ÀÌÅÍÀÇ Â÷¿øÀ» ÁÙÀÌ¸é¼ ÃÖ´ëÀÇ µ¥ÀÌÅÍ º¯À̸¦ º¸Á¸ÇÏ´Â ±â¹ýÀ¸·Î Â÷¿ø Ãà¼Ò³ª ÇÇó ÃßÃâÀ» À§ÇØ ³Î¸® »ç¿ëµÇ°í ÀÖ´Ù. ÇÏÁö¸¸ PCA´Â ÀâÀ½¿¡ ¹Î°¨ÇÑ ´ÜÁ¡ÀÌ ÀÖÀ¸¸ç, ÀÌ·¯ÇÑ ÀâÀ½ ¹Î°¨¼ºÀ» ÇØ°áÇϱâ À§ÇØ ¿©·¯ °¡Áö PCA º¯ÇüÀÌ Á¦¾ÈµÇ¾ú´Ù. ±× Áß robust fuzzy PCA(RF-PCA)´Â ÆÛÁö ¼Ò¼Óµµ¸¦ »ç¿ëÇÏ¿© ÀâÀ½ÀÇ ¿µÇâÀ» È¿°úÀûÀ¸·Î ÁÙÀÏ ¼ö ÀÖÀ½ÀÌ ÀÔÁõµÇ¾ú´Ù. ÇÏÁö¸¸ RF-PCA ¿ª½Ã ¸î °¡Áö ¹®Á¦Á¡ÀÌ ÀÖ°í, ¼ö·Å¼ºÀÌ ±× Áß ÇϳªÀÌ´Ù. RF-PCA´Â ¼Ò¼Óµµ¿Í ÁÖ¼ººÐÀ» °»½ÅÇÒ ¶§ ¼·Î ´Ù¸¥ ¸ñÀû ÇÔ¼ö¸¦ »ç¿ëÇϹǷΠ¼ö·Å ¼Óµµ°¡ ´À¸®°í ±¸ÇØÁö´Â ÇØ°¡ ±¹ºÎ ÃÖÀûÇØÀÓÀ» º¸ÀåÇÏÁö ¾Ê´Â´Ù. ÀÌ ³í¹®¿¡¼´Â RF-PCAÀÇ ¹®Á¦Á¡À» ÇØ°áÇϱâ À§ÇØ ÇϳªÀÇ ¸ñÀû ÇÔ¼ö¸¦ ÀÌ¿ëÇØ ¼Ò¼Óµµ¿Í ÁÖ¼ººÐÀ» °»½ÅÇÒ ¼ö ÀÖ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÑ ¹æ¹ý, RF-PCA2´Â ¹Ýº¹ ÃÖÀûȸ¦ ÀÌ¿ëÇÔÀ¸·Î½á ±¹ºÎ ÃÖÀûÇØ¿¡ ¼ö·ÅÇÔÀ» º¸ÀåÇϸç, RF-PCA¿¡ ºñÇØ ºü¸¥ ¼ö·Å ¼Óµµ¸¦ °¡Áö°í, ÀâÀ½ ¹Î°¨¼ºÀÌ ÁÙ¾îµç´Ù. ÀÌ·¯ÇÑ »ç½ÇµéÀº ½ÇÇè °á°ú¸¦ ÅëÇØ È®ÀÎÇÒ ¼ö ÀÖ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
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.
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Å°¿öµå(Keyword) |
ÁÖ¼ººÐ ºÐ¼®
ÆÛÁö ¼Ò¼Óµµ
¼ö·Å¼º
ÀâÀ½ ¹Î°¨¼º
Robust ÆÛÁö PCA
Principal Component Analysis
Fuzzy Membership
Convergence Property
Noise Sensitivity
Robust Fuzzy PCA
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