{"id":476841,"date":"2023-08-09T07:36:15","date_gmt":"2023-08-09T07:36:15","guid":{"rendered":""},"modified":"2023-09-05T11:13:31","modified_gmt":"2023-09-05T11:13:31","slug":"dimensionality-reduction","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/dimensionality-reduction\/","title":{"rendered":"Gi\u1ea3m k\u00edch th\u01b0\u1edbc"},"content":{"rendered":"<h2>Gi\u1edbi thi\u1ec7u<\/h2>\n<p>Gi\u1ea3m k\u00edch th\u01b0\u1edbc l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt quan tr\u1ecdng trong l\u0129nh v\u1ef1c ph\u00e2n t\u00edch d\u1eef li\u1ec7u v\u00e0 h\u1ecdc m\u00e1y nh\u1eb1m m\u1ee5c \u0111\u00edch \u0111\u01a1n gi\u1ea3n h\u00f3a c\u00e1c b\u1ed9 d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p trong khi v\u1eabn gi\u1eef \u0111\u01b0\u1ee3c th\u00f4ng tin ph\u00f9 h\u1ee3p nh\u1ea5t. Khi c\u00e1c t\u1eadp d\u1eef li\u1ec7u t\u0103ng v\u1ec1 k\u00edch th\u01b0\u1edbc v\u00e0 \u0111\u1ed9 ph\u1ee9c t\u1ea1p, ch\u00fang th\u01b0\u1eddng g\u1eb7p ph\u1ea3i \u201cl\u1eddi nguy\u1ec1n v\u1ec1 chi\u1ec1u\u201d, d\u1eabn \u0111\u1ebfn t\u0103ng th\u1eddi gian t\u00ednh to\u00e1n, s\u1eed d\u1ee5ng b\u1ed9 nh\u1edb v\u00e0 gi\u1ea3m hi\u1ec7u su\u1ea5t c\u1ee7a c\u00e1c thu\u1eadt to\u00e1n h\u1ecdc m\u00e1y. K\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc \u0111\u01b0a ra gi\u1ea3i ph\u00e1p b\u1eb1ng c\u00e1ch chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u c\u00f3 chi\u1ec1u cao th\u00e0nh kh\u00f4ng gian c\u00f3 chi\u1ec1u th\u1ea5p h\u01a1n, gi\u00fap d\u1ec5 d\u00e0ng h\u00ecnh dung, x\u1eed l\u00fd v\u00e0 ph\u00e2n t\u00edch h\u01a1n.<\/p>\n<h2>L\u1ecbch s\u1eed c\u1ee7a vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/h2>\n<p>Kh\u00e1i ni\u1ec7m gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 t\u1eeb nh\u1eefng ng\u00e0y \u0111\u1ea7u c\u1ee7a th\u1ed1ng k\u00ea v\u00e0 to\u00e1n h\u1ecdc. M\u1ed9t trong nh\u1eefng \u0111\u1ec1 c\u1eadp \u0111\u1ea7u ti\u00ean v\u1ec1 vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb c\u00f4ng tr\u00ecnh c\u1ee7a Karl Pearson v\u00e0o \u0111\u1ea7u nh\u1eefng n\u0103m 1900, n\u01a1i \u00f4ng \u0111\u01b0a ra kh\u00e1i ni\u1ec7m ph\u00e2n t\u00edch th\u00e0nh ph\u1ea7n ch\u00ednh (PCA). Tuy nhi\u00ean, s\u1ef1 ph\u00e1t tri\u1ec3n r\u1ed9ng r\u00e3i h\u01a1n c\u1ee7a c\u00e1c thu\u1eadt to\u00e1n gi\u1ea3m k\u00edch th\u01b0\u1edbc \u0111\u00e3 \u0111\u1ea1t \u0111\u01b0\u1ee3c \u0111\u1ed9ng l\u1ef1c v\u00e0o gi\u1eefa th\u1ebf k\u1ef7 20 v\u1edbi s\u1ef1 ra \u0111\u1eddi c\u1ee7a m\u00e1y t\u00ednh v\u00e0 m\u1ed1i quan t\u00e2m ng\u00e0y c\u00e0ng t\u0103ng \u0111\u1ed1i v\u1edbi ph\u00e2n t\u00edch d\u1eef li\u1ec7u \u0111a bi\u1ebfn.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 Gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/h2>\n<p>C\u00e1c ph\u01b0\u01a1ng ph\u00e1p gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e2n lo\u1ea1i th\u00e0nh hai lo\u1ea1i: l\u1ef1a ch\u1ecdn t\u00ednh n\u0103ng v\u00e0 tr\u00edch xu\u1ea5t t\u00ednh n\u0103ng. C\u00e1c ph\u01b0\u01a1ng ph\u00e1p l\u1ef1a ch\u1ecdn \u0111\u1ed1i t\u01b0\u1ee3ng ch\u1ecdn m\u1ed9t t\u1eadp h\u1ee3p con c\u1ee7a c\u00e1c \u0111\u1ed1i t\u01b0\u1ee3ng ban \u0111\u1ea7u, trong khi c\u00e1c ph\u01b0\u01a1ng ph\u00e1p tr\u00edch xu\u1ea5t \u0111\u1ed1i t\u01b0\u1ee3ng s\u1ebd chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u th\u00e0nh kh\u00f4ng gian \u0111\u1ed1i t\u01b0\u1ee3ng m\u1edbi.<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/h2>\n<p>Nguy\u00ean l\u00fd l\u00e0m vi\u1ec7c c\u1ee7a k\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 th\u1ec3 kh\u00e1c nhau t\u00f9y thu\u1ed9c v\u00e0o ph\u01b0\u01a1ng ph\u00e1p \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng. M\u1ed9t s\u1ed1 ph\u01b0\u01a1ng ph\u00e1p nh\u01b0 PCA t\u00ecm c\u00e1ch t\u00ecm ra m\u1ed9t ph\u00e9p bi\u1ebfn \u0111\u1ed5i tuy\u1ebfn t\u00ednh gi\u00fap t\u1ed1i \u0111a h\u00f3a ph\u01b0\u01a1ng sai trong kh\u00f4ng gian \u0111\u1eb7c tr\u01b0ng m\u1edbi. Nh\u1eefng gi\u1ea3i ph\u00e1p kh\u00e1c, ch\u1eb3ng h\u1ea1n nh\u01b0 Stochastic Neighbor Embedding ph\u00e2n ph\u1ed1i t (t-SNE), t\u1eadp trung v\u00e0o vi\u1ec7c duy tr\u00ec s\u1ef1 t\u01b0\u01a1ng \u0111\u1ed3ng theo c\u1eb7p gi\u1eefa c\u00e1c \u0111i\u1ec3m d\u1eef li\u1ec7u trong qu\u00e1 tr\u00ecnh chuy\u1ec3n \u0111\u1ed5i.<\/p>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/h2>\n<p>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a k\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c t\u00f3m t\u1eaft nh\u01b0 sau:<\/p>\n<ol>\n<li><strong>Gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/strong>: Gi\u1ea3m s\u1ed1 l\u01b0\u1ee3ng t\u00ednh n\u0103ng trong khi v\u1eabn duy tr\u00ec c\u00e1c th\u00f4ng tin c\u1ea7n thi\u1ebft trong d\u1eef li\u1ec7u.<\/li>\n<li><strong>M\u1ea5t th\u00f4ng tin<\/strong>: V\u1ed1n c\u00f3 trong quy tr\u00ecnh, v\u00ec vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 th\u1ec3 d\u1eabn \u0111\u1ebfn m\u1ea5t m\u1ed9t s\u1ed1 th\u00f4ng tin.<\/li>\n<li><strong>Hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n<\/strong>: T\u0103ng t\u1ed1c c\u00e1c thu\u1eadt to\u00e1n ho\u1ea1t \u0111\u1ed9ng tr\u00ean d\u1eef li\u1ec7u c\u00f3 chi\u1ec1u th\u1ea5p h\u01a1n, cho ph\u00e9p x\u1eed l\u00fd nhanh h\u01a1n.<\/li>\n<li><strong>H\u00ecnh dung<\/strong>: T\u1ea1o \u0111i\u1ec1u ki\u1ec7n tr\u1ef1c quan h\u00f3a d\u1eef li\u1ec7u trong kh\u00f4ng gian c\u00f3 chi\u1ec1u th\u1ea5p h\u01a1n, h\u1ed7 tr\u1ee3 hi\u1ec3u c\u00e1c b\u1ed9 d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p.<\/li>\n<li><strong>Gi\u1ea3m ti\u1ebfng \u1ed3n<\/strong>: M\u1ed9t s\u1ed1 ph\u01b0\u01a1ng ph\u00e1p gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 th\u1ec3 tri\u1ec7t ti\u00eau nhi\u1ec5u v\u00e0 t\u1eadp trung v\u00e0o c\u00e1c m\u1eabu c\u01a1 b\u1ea3n.<\/li>\n<\/ol>\n<h2>C\u00e1c lo\u1ea1i gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/h2>\n<p>C\u00f3 m\u1ed9t s\u1ed1 k\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc, m\u1ed7i k\u1ef9 thu\u1eadt \u0111\u1ec1u c\u00f3 \u0111i\u1ec3m m\u1ea1nh v\u00e0 \u0111i\u1ec3m y\u1ebfu. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 danh s\u00e1ch m\u1ed9t s\u1ed1 ph\u01b0\u01a1ng ph\u00e1p ph\u1ed5 bi\u1ebfn:<\/p>\n<table>\n<thead>\n<tr>\n<th>Ph\u01b0\u01a1ng ph\u00e1p<\/th>\n<th>Ki\u1ec3u<\/th>\n<th>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ph\u00e2n t\u00edch th\u00e0nh ph\u1ea7n ch\u00ednh (PCA)<\/td>\n<td>tuy\u1ebfn t\u00ednh<\/td>\n<td>N\u1eafm b\u1eaft ph\u01b0\u01a1ng sai t\u1ed1i \u0111a trong c\u00e1c th\u00e0nh ph\u1ea7n tr\u1ef1c giao<\/td>\n<\/tr>\n<tr>\n<td>t-Nh\u00fang h\u00e0ng x\u00f3m ng\u1eabu nhi\u00ean ph\u00e2n t\u00e1n (t-SNE)<\/td>\n<td>Phi tuy\u1ebfn t\u00ednh<\/td>\n<td>B\u1ea3o t\u1ed3n s\u1ef1 t\u01b0\u01a1ng \u0111\u1ed3ng theo c\u1eb7p<\/td>\n<\/tr>\n<tr>\n<td>B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/td>\n<td>D\u1ef1a tr\u00ean m\u1ea1ng th\u1ea7n kinh<\/td>\n<td>T\u00ecm hi\u1ec3u c\u00e1c ph\u00e9p bi\u1ebfn \u0111\u1ed5i phi tuy\u1ebfn t\u00ednh<\/td>\n<\/tr>\n<tr>\n<td>Ph\u00e2n t\u00e1ch gi\u00e1 tr\u1ecb s\u1ed1 \u00edt (SVD)<\/td>\n<td>H\u1ec7 s\u1ed1 h\u00f3a ma tr\u1eadn<\/td>\n<td>H\u1eefu \u00edch cho vi\u1ec7c l\u1ecdc c\u1ed9ng t\u00e1c v\u00e0 n\u00e9n h\u00ecnh \u1ea3nh<\/td>\n<\/tr>\n<tr>\n<td>B\u1ea3n \u0111\u1ed3 \u0111\u1ed3ng ph\u00e2n<\/td>\n<td>H\u1ecdc t\u1eadp \u0111a d\u1ea1ng<\/td>\n<td>B\u1ea3o to\u00e0n kho\u1ea3ng c\u00e1ch tr\u1eafc \u0111\u1ecba<\/td>\n<\/tr>\n<tr>\n<td>Nh\u00fang tuy\u1ebfn t\u00ednh c\u1ee5c b\u1ed9 (LLE)<\/td>\n<td>H\u1ecdc t\u1eadp \u0111a d\u1ea1ng<\/td>\n<td>Duy tr\u00ec c\u00e1c m\u1ed1i quan h\u1ec7 c\u1ee5c b\u1ed9 trong d\u1eef li\u1ec7u<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng Gi\u1ea3m k\u00edch th\u01b0\u1edbc v\u00e0 nh\u1eefng th\u00e1ch th\u1ee9c<\/h2>\n<p>Gi\u1ea3m k\u00edch th\u01b0\u1edbc c\u00f3 nhi\u1ec1u \u1ee9ng d\u1ee5ng kh\u00e1c nhau tr\u00ean c\u00e1c l\u0129nh v\u1ef1c kh\u00e1c nhau, ch\u1eb3ng h\u1ea1n nh\u01b0 x\u1eed l\u00fd h\u00ecnh \u1ea3nh, x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean v\u00e0 h\u1ec7 th\u1ed1ng \u0111\u1ec1 xu\u1ea5t. M\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p s\u1eed d\u1ee5ng ph\u1ed5 bi\u1ebfn bao g\u1ed3m:<\/p>\n<ol>\n<li><strong>Tr\u1ef1c quan h\u00f3a d\u1eef li\u1ec7u<\/strong>: Bi\u1ec3u di\u1ec5n d\u1eef li\u1ec7u chi\u1ec1u cao trong kh\u00f4ng gian chi\u1ec1u th\u1ea5p h\u01a1n \u0111\u1ec3 tr\u1ef1c quan h\u00f3a c\u00e1c c\u1ee5m v\u00e0 m\u1eabu.<\/li>\n<li><strong>K\u1ef9 thu\u1eadt t\u00ednh n\u0103ng<\/strong>: B\u01b0\u1edbc ti\u1ec1n x\u1eed l\u00fd \u0111\u1ec3 c\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t c\u1ee7a m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y b\u1eb1ng c\u00e1ch gi\u1ea3m nhi\u1ec5u v\u00e0 d\u01b0 th\u1eeba.<\/li>\n<li><strong>Ph\u00e2n c\u1ee5m<\/strong>: X\u00e1c \u0111\u1ecbnh c\u00e1c nh\u00f3m \u0111i\u1ec3m d\u1eef li\u1ec7u t\u01b0\u01a1ng t\u1ef1 d\u1ef1a tr\u00ean k\u00edch th\u01b0\u1edbc gi\u1ea3m.<\/li>\n<\/ol>\n<p>Nh\u1eefng th\u00e1ch th\u1ee9c v\u00e0 gi\u1ea3i ph\u00e1p:<\/p>\n<ul>\n<li><strong>M\u1ea5t th\u00f4ng tin<\/strong>: V\u00ec vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc s\u1ebd lo\u1ea1i b\u1ecf m\u1ed9t s\u1ed1 th\u00f4ng tin n\u00ean \u0111i\u1ec1u quan tr\u1ecdng l\u00e0 ph\u1ea3i \u0111\u1ea1t \u0111\u01b0\u1ee3c s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc v\u00e0 b\u1ea3o to\u00e0n th\u00f4ng tin.<\/li>\n<li><strong>\u0110\u1ed9 ph\u1ee9c t\u1ea1p t\u00ednh to\u00e1n<\/strong>: \u0110\u1ed1i v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn, m\u1ed9t s\u1ed1 ph\u01b0\u01a1ng ph\u00e1p c\u00f3 th\u1ec3 t\u1ed1n k\u00e9m v\u1ec1 m\u1eb7t t\u00ednh to\u00e1n. X\u1ea5p x\u1ec9 v\u00e0 song song h\u00f3a c\u00f3 th\u1ec3 gi\u00fap gi\u1ea3m thi\u1ec3u v\u1ea5n \u0111\u1ec1 n\u00e0y.<\/li>\n<li><strong>D\u1eef li\u1ec7u phi tuy\u1ebfn t\u00ednh<\/strong>: C\u00e1c ph\u01b0\u01a1ng ph\u00e1p tuy\u1ebfn t\u00ednh c\u00f3 th\u1ec3 kh\u00f4ng ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c b\u1ed9 d\u1eef li\u1ec7u c\u00f3 t\u00ednh phi tuy\u1ebfn t\u00ednh cao, \u0111\u00f2i h\u1ecfi ph\u1ea3i s\u1eed d\u1ee5ng c\u00e1c k\u1ef9 thu\u1eadt phi tuy\u1ebfn t\u00ednh nh\u01b0 t-SNE.<\/li>\n<\/ul>\n<h2>\u0110\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 so s\u00e1nh<\/h2>\n<p>D\u01b0\u1edbi \u0111\u00e2y l\u00e0 so s\u00e1nh gi\u1eefa vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc v\u00e0 c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1:<\/p>\n<table>\n<thead>\n<tr>\n<th>Thu\u1eadt ng\u1eef<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/td>\n<td>K\u1ef9 thu\u1eadt gi\u1ea3m s\u1ed1 l\u01b0\u1ee3ng \u0111\u1eb7c tr\u01b0ng trong d\u1eef li\u1ec7u.<\/td>\n<\/tr>\n<tr>\n<td>L\u1ef1a ch\u1ecdn t\u00ednh n\u0103ng<\/td>\n<td>Ch\u1ecdn m\u1ed9t t\u1eadp h\u1ee3p con c\u00e1c t\u00ednh n\u0103ng ban \u0111\u1ea7u d\u1ef1a tr\u00ean m\u1ee9c \u0111\u1ed9 li\u00ean quan.<\/td>\n<\/tr>\n<tr>\n<td>Khai th\u00e1c t\u00ednh n\u0103ng<\/td>\n<td>Chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u th\u00e0nh m\u1ed9t kh\u00f4ng gian t\u00ednh n\u0103ng m\u1edbi.<\/td>\n<\/tr>\n<tr>\n<td>N\u00e9n d\u1eef li\u1ec7u<\/td>\n<td>Gi\u1ea3m k\u00edch th\u01b0\u1edbc d\u1eef li\u1ec7u trong khi v\u1eabn gi\u1eef \u0111\u01b0\u1ee3c th\u00f4ng tin quan tr\u1ecdng.<\/td>\n<\/tr>\n<tr>\n<td>Chi\u1ebfu d\u1eef li\u1ec7u<\/td>\n<td>\u00c1nh x\u1ea1 d\u1eef li\u1ec7u t\u1eeb kh\u00f4ng gian c\u00f3 chi\u1ec1u cao h\u01a1n sang kh\u00f4ng gian c\u00f3 chi\u1ec1u th\u1ea5p h\u01a1n.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 t\u01b0\u01a1ng lai<\/h2>\n<p>T\u01b0\u01a1ng lai c\u1ee7a vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc n\u1eb1m \u1edf vi\u1ec7c ph\u00e1t tri\u1ec3n c\u00e1c thu\u1eadt to\u00e1n hi\u1ec7u qu\u1ea3 v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c b\u1ed9 d\u1eef li\u1ec7u ng\u00e0y c\u00e0ng l\u1edbn v\u00e0 ph\u1ee9c t\u1ea1p. Nghi\u00ean c\u1ee9u v\u1ec1 c\u00e1c k\u1ef9 thu\u1eadt phi tuy\u1ebfn t\u00ednh, thu\u1eadt to\u00e1n t\u1ed1i \u01b0u h\u00f3a v\u00e0 t\u0103ng t\u1ed1c ph\u1ea7n c\u1ee9ng c\u00f3 th\u1ec3 s\u1ebd d\u1eabn \u0111\u1ebfn nh\u1eefng ti\u1ebfn b\u1ed9 \u0111\u00e1ng k\u1ec3 trong l\u0129nh v\u1ef1c n\u00e0y. Ngo\u00e0i ra, vi\u1ec7c k\u1ebft h\u1ee3p gi\u1ea3m k\u00edch th\u01b0\u1edbc v\u1edbi c\u00e1c ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc s\u00e2u h\u1ee9a h\u1eb9n t\u1ea1o ra c\u00e1c m\u00f4 h\u00ecnh m\u1ea1nh m\u1ebd v\u00e0 bi\u1ec3u c\u1ea3m h\u01a1n.<\/p>\n<h2>M\u00e1y ch\u1ee7 proxy v\u00e0 gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy, gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p, c\u00f3 th\u1ec3 h\u01b0\u1edfng l\u1ee3i gi\u00e1n ti\u1ebfp t\u1eeb c\u00e1c k\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc. M\u1eb7c d\u00f9 ch\u00fang c\u00f3 th\u1ec3 kh\u00f4ng \u0111\u01b0\u1ee3c li\u00ean k\u1ebft tr\u1ef1c ti\u1ebfp nh\u01b0ng vi\u1ec7c s\u1eed d\u1ee5ng t\u00ednh n\u0103ng gi\u1ea3m k\u00edch th\u01b0\u1edbc trong d\u1eef li\u1ec7u ti\u1ec1n x\u1eed l\u00fd c\u00f3 th\u1ec3 c\u1ea3i thi\u1ec7n hi\u1ec7u qu\u1ea3 v\u00e0 t\u1ed1c \u0111\u1ed9 t\u1ed5ng th\u1ec3 c\u1ee7a m\u00e1y ch\u1ee7 proxy, d\u1eabn \u0111\u1ebfn hi\u1ec7u su\u1ea5t \u0111\u01b0\u1ee3c n\u00e2ng cao v\u00e0 tr\u1ea3i nghi\u1ec7m ng\u01b0\u1eddi d\u00f9ng t\u1ed1t h\u01a1n.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<p>\u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 vi\u1ec7c gi\u1ea3m k\u00edch th\u01b0\u1edbc, b\u1ea1n c\u00f3 th\u1ec3 kh\u00e1m ph\u00e1 c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n<ul>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Principal_component_analysis\" target=\"_new\" rel=\"noopener nofollow\">PCA \u2013 Ph\u00e2n t\u00edch th\u00e0nh ph\u1ea7n ch\u00ednh<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/T-distributed_stochastic_neighbor_embedding\" target=\"_new\" rel=\"noopener nofollow\">t-SNE<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Autoencoder\" target=\"_new\" rel=\"noopener nofollow\">B\u1ed9 m\u00e3 h\u00f3a t\u1ef1 \u0111\u1ed9ng<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Singular_value_decomposition\" target=\"_new\" rel=\"noopener nofollow\">SVD \u2013 Ph\u00e2n t\u00e1ch gi\u00e1 tr\u1ecb s\u1ed1 \u00edt<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Isomap\" target=\"_new\" rel=\"noopener nofollow\">B\u1ea3n \u0111\u1ed3 \u0111\u1ed3ng ph\u00e2n<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Local_linear_embedding\" target=\"_new\" rel=\"noopener nofollow\">LLE - Nh\u00fang tuy\u1ebfn t\u00ednh c\u1ee5c b\u1ed9<\/a><\/li>\n<\/ul>\n<p>T\u00f3m l\u1ea1i, gi\u1ea3m k\u00edch th\u01b0\u1edbc l\u00e0 m\u1ed9t c\u00f4ng c\u1ee5 thi\u1ebft y\u1ebfu trong l\u0129nh v\u1ef1c ph\u00e2n t\u00edch d\u1eef li\u1ec7u v\u00e0 h\u1ecdc m\u00e1y. B\u1eb1ng c\u00e1ch chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u chi\u1ec1u cao th\u00e0nh c\u00e1c bi\u1ec3u di\u1ec5n chi\u1ec1u th\u1ea5p h\u01a1n c\u00f3 th\u1ec3 qu\u1ea3n l\u00fd v\u00e0 cung c\u1ea5p th\u00f4ng tin, c\u00e1c k\u1ef9 thu\u1eadt gi\u1ea3m k\u00edch th\u01b0\u1edbc s\u1ebd m\u1edf kh\u00f3a nh\u1eefng hi\u1ec3u bi\u1ebft s\u00e2u s\u1eafc h\u01a1n, t\u0103ng t\u1ed1c t\u00ednh to\u00e1n v\u00e0 \u0111\u00f3ng g\u00f3p v\u00e0o nh\u1eefng ti\u1ebfn b\u1ed9 trong c\u00e1c ng\u00e0nh kh\u00e1c nhau.<\/p>","protected":false},"featured_media":468229,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476841","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Dimensionality Reduction: Unraveling the Complexity of Data<\/mark>","faq_items":[{"question":"What is dimensionality reduction, and why is it essential?","answer":"<p>Dimensionality reduction is a technique used in data analysis and machine learning to simplify complex datasets by reducing the number of features while retaining relevant information. It is essential because high-dimensional data can lead to computational inefficiencies, memory issues, and reduced performance of algorithms. Dimensionality reduction helps in visualizing and processing data more efficiently.<\/p>"},{"question":"How did dimensionality reduction originate?","answer":"<p>The concept of dimensionality reduction has roots in the early 20th century, with Karl Pearson's work on principal component analysis (PCA). However, the broader development of dimensionality reduction algorithms gained momentum in the mid-20th century with the rise of computers and multivariate data analysis.<\/p>"},{"question":"How do dimensionality reduction techniques work?","answer":"<p>Dimensionality reduction methods can be categorized into feature selection and feature extraction. Feature selection methods choose a subset of the original features, while feature extraction methods transform the data into a new feature space. Techniques like PCA aim to find a linear transformation that maximizes variance, while others, like t-SNE, focus on preserving pairwise similarities between data points.<\/p>"},{"question":"What are the key features of dimensionality reduction techniques?","answer":"<p>The key features of dimensionality reduction include reducing dimensionality, computational efficiency, noise reduction, and facilitating data visualization. However, it's important to note that dimensionality reduction may lead to some loss of information.<\/p>"},{"question":"What types of dimensionality reduction techniques are there?","answer":"<p>There are several types of dimensionality reduction techniques, each with its strengths. Some popular ones are:<\/p><ol><li>Principal Component Analysis (PCA) - Linear<\/li><li>t-Distributed Stochastic Neighbor Embedding (t-SNE) - Non-linear<\/li><li>Autoencoders - Neural Network-based<\/li><li>Singular Value Decomposition (SVD) - Matrix Factorization<\/li><li>Isomap - Manifold Learning<\/li><li>Locally Linear Embedding (LLE) - Manifold Learning<\/li><\/ol>"},{"question":"How can dimensionality reduction be used, and what challenges does it present?","answer":"<p>Dimensionality reduction finds applications in data visualization, feature engineering, and clustering. Challenges include information loss, computational complexity, and the suitability of linear methods for non-linear data. Solutions involve balancing information preservation and approximation techniques.<\/p>"},{"question":"How does dimensionality reduction compare with similar terms?","answer":"<p>Dimensionality reduction is closely related to feature selection, feature extraction, data compression, and data projection. While they share similarities, each term addresses specific aspects of data manipulation.<\/p>"},{"question":"What is the future of dimensionality reduction?","answer":"<p>The future of dimensionality reduction lies in developing more efficient algorithms, non-linear techniques, and leveraging deep learning approaches. Advancements in hardware acceleration and optimization will contribute to handling increasingly large and complex datasets effectively.<\/p>"},{"question":"How are proxy servers associated with dimensionality reduction?","answer":"<p>Though not directly associated, proxy servers like OneProxy can indirectly benefit from dimensionality reduction's preprocessing advantages. Using dimensionality reduction can improve the overall efficiency and speed of proxy servers, leading to enhanced performance and user experience.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476841","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476841\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468229"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=476841"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}