\u72ec\u7acb\u6210\u5206\u5206\u6790ICA<\/h3>\n\n\n\n
\u72ec\u7acb\u5206\u91cf\u5206\u6790\uff08Independent Component Analysis\uff0cICA\uff09\u662f\u5c06\u4fe1\u53f7\u4e4b\u95f4\u7684\u72ec\u7acb\u6027\u4f5c\u4e3a\u5206\u79bb\u53d8\u91cf\u5224\u636e\u7684\u65b9\u6cd5\u3002\u7531Comon\u4e8e1994\u5e74\u9996\u6b21\u63d0\u51fa\u3002Comon\u6307\u51faICA\u65b9\u6cd5\u53ef\u4ee5\u901a\u8fc7\u67d0\u4e2a\u5bf9\u6bd4\u51fd\u6570\uff08Contrast Function\uff09\u7684\u76ee\u6807\u51fd\u6570\u8fbe\u5230\u6781\u5927\u503c\u6765\u6d88\u9664\u89c2\u5bdf\u4fe1\u53f7\u4e2d\u7684\u9ad8\u9636\u7edf\u8ba1\u5173\u8054\uff0c\u5b9e\u73b0\u76f2\u6e90\u5206\u79bb\u3002\u76f2\u6e90\u5206\u79bb\u88ab\u63cf\u8ff0\u6210\u5728\u4e0d\u77e5\u9053\u4f20\u8f93\u901a\u9053\u7279\u6027\u7684\u60c5\u51b5\u4e0b\uff0c\u4ece\u4f20\u611f\u5668\u6216\u4f20\u611f\u5668\u9635\u5217\u4e2d\u5206\u79bb\u6216\u4f30\u8ba1\u539f\u59cb\u6e90\u6ce2\u5f62\u7684\u95ee\u9898\u3002\u7136\u800c\uff0c\u5bf9\u4e8e\u67d0\u4e9b\u6a21\u578b\uff0c\u4e0d\u80fd\u4fdd\u8bc1\u4f30\u8ba1\u6216\u63d0\u53d6\u7684\u4fe1\u53f7\u4e0e\u6e90\u4fe1\u53f7\u5177\u6709\u5b8c\u5168\u76f8\u540c\u7684\u6ce2\u5f62\uff0c\u56e0\u6b64\u6709\u65f6\u8981\u6c42\u653e\u5bbd\u5230\u63d0\u53d6\u7684\u6ce2\u5f62\u662f\u6e90\u4fe1\u53f7\u7684\u5931\u771f\u6216\u6ee4\u6ce2\u7248\u672c\u3002<\/p>\n\n\n\n \u72ec\u7acb\u6210\u5206\u5206\u6790\uff08Independent Component Analysis\uff09\uff0c\u6700\u65e9\u5e94\u7528\u4e8e\u76f2\u6e90\u4fe1\u53f7\u5206\u79bb\uff08Blind Source Separation\uff0cBBS\uff09\u3002\u8d77\u6e90\u4e8e\u201c\u9e21\u5c3e\u9152\u4f1a\u95ee\u9898<\/strong>\u201d\uff0c\u63cf\u8ff0\u5982\u4e0b\uff1a\u5728\u5608\u6742\u7684\u9e21\u5c3e\u9152\u4f1a\u4e0a\uff0c\u8bb8\u591a\u4eba\u5728\u540c\u65f6\u4ea4\u8c08\uff0c\u53ef\u80fd\u8fd8\u6709\u80cc\u666f\u97f3\u4e50\uff0c\u4f46\u4eba\u8033\u5374\u80fd\u51c6\u786e\u800c\u6e05\u6670\u7684\u542c\u5230\u5bf9\u65b9\u7684\u8bdd\u8bed\u3002\u8fd9\u79cd\u53ef\u4ee5\u4ece\u6df7\u5408\u58f0\u97f3\u4e2d\u9009\u62e9\u81ea\u5df1\u611f\u5174\u8da3\u7684\u58f0\u97f3\u800c\u5ffd\u7565\u5176\u4ed6\u58f0\u97f3\u7684\u73b0\u8c61\u79f0\u4e3a\u201c\u9e21\u5c3e\u9152\u4f1a\u6548\u5e94\u201d\u3002 ICA\u7684python\u5b9e\u73b0<\/p>\n\n\n\n \u5e94\u7528Python\u673a\u5668\u5b66\u4e60\u5e93SKlearn\u4e2d\u7684FastICA\u6765\u6f14\u793a\u4fe1\u53f7\u5206\u79bb\u5e76\u4e0ePCA\u8fdb\u884c\u5bf9\u6bd4\u3002<\/p>\n\n\n\n from sklearn.decomposition import FastICA \u4ee5\u4e0a\u662f FastICA \u6a21\u578b\u901a\u5e38\u6240\u542b\u7684\u53c2\u6570\u53ca\u5176\u5bf9\u5e94\u7684\u9ed8\u8ba4\u503c\u3002 n_components: \u6307\u5b9a\u4f7f\u7528\u5143\u7d20\u7684\u6570\u76ee\u3002 algorithm: {‘parallel’,’deflational’},\u6307\u5b9a FastICA \u4f7f\u7528\u54ea\u79cd\u7b97\u6cd5\u3002 writen: True\/False\uff0c\u662f\u5426\u8fdb\u884c\u767d\u5316\u5904\u7406\u3002 fun: {‘logcosh’,’exp’,’cube’,..}\uff0c\u9009\u62e9\u4e00\u79cd\u8fd1\u4f3c\u4e8e\u8ba1\u7b97\u8d1f\u71b5\u7684\u76ee\u6807\u51fd\u6570\uff0c\u53ef\u81ea\u5df1\u5b9a\u4e49\u51fd\u6570\u3002 fun_args: \u6307\u5b9a\u76ee\u6807\u51fd\u6570\u6240\u8981\u7528\u7684\u53c2\u6570\u3002 max_iter: \u6307\u5b9a\u62df\u5408\u8fc7\u7a0b\u4e2d\u6700\u5927\u7684\u8fed\u4ee3\u6b21\u6570\u3002 w_init: \u6307\u5b9a\u521d\u59cb\u7684\u6df7\u5408\u77e9\u9635\u3002<\/p>\n\n\n\n \u200b 1. \u4e3b\u6210\u5206\u5206\u6790\u5047\u8bbe\u6e90\u4fe1\u53f7\u95f4\u5f7c\u6b64\u975e\u76f8\u5173\uff0c\u72ec\u7acb\u6210\u5206\u5206\u6790\u5047\u8bbe\u6e90\u4fe1\u53f7\u95f4\u5f7c\u6b64\u72ec\u7acb\u3002<\/strong><\/p>\n\n\n\n \u200b 2. \u4e3b\u6210\u5206\u5206\u6790\u8ba4\u4e3a\u4e3b\u5143\u4e4b\u95f4\u5f7c\u6b64\u6b63\u4ea4\uff0c\u6837\u672c\u5448\u9ad8\u65af\u5206\u5e03\uff1b\u72ec\u7acb\u6210\u5206\u5206\u6790\u5219\u4e0d\u8981\u6c42\u6837\u672c\u5448\u9ad8\u65af\u5206\u5e03\u3002<\/strong><\/p>\n\n\n\n \u7a00\u758f\u4e3b\u6210\u5206\u5206\u6790\u5373\u662f\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u800c\u5f15\u8fdb\u7684\u4e00\u4e2a\u7b97\u6cd5\u3002\u5b83\u4f1a\u628a\u4e3b\u6210\u5206\u7cfb\u6570\uff08\u6784\u6210\u4e3b\u6210\u5206\u65f6\u6bcf\u4e2a\u53d8\u91cf\u524d\u9762\u7684\u7cfb\u6570\uff09\u53d8\u7684\u7a00\u758f\uff0c\u4e5f\u5373\u662f\u628a\u5927\u591a\u6570\u7cfb\u6570\u90fd\u53d8\u6210\u96f6\uff0c\u901a\u8fc7\u8fd9\u6837\u4e00\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u628a\u4e3b\u6210\u5206\u7684\u4e3b\u8981\u7684\u90e8\u5206\u51f8\u73b0\u51fa\u6765\uff0c\u8fd9\u6837\u4e3b\u6210\u5206\u5c31\u4f1a\u53d8\u5f97\u8f83\u4e3a\u5bb9\u6613\u89e3\u91ca\u3002<\/p>\n\n\n\n \u5b9e\u73b0\u4e3b\u6210\u5206\u5206\u6790\u7a00\u758f\u5316\uff0c\u6700\u7ec8\u4f1a\u8f6c\u5316\u4e3a\u4f18\u5316\u95ee\u9898\uff0c \u4e5f\u5373\u5bf9\u672c\u6765\u7684\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u4e2d\u7684\u95ee\u9898\u589e\u52a0\u4e00\u4e2a\u60e9\u7f5a\u51fd\u6570\u3002 \u8fd9\u4e2a\u60e9\u7f5a\u51fd\u6570\u5305\u542b\u6709\u7a00\u758f\u5ea6\u7684\u4fe1\u606f\u3002\u5f53\u7136\uff0c\u6700\u7ec8\u5f97\u5230\u7684\u95ee\u9898\u662fNP-\u56f0\u96be\u95ee\u9898\uff0c\u4e3a\u4e86\u89e3\u51b3\u5b83\uff0c\u6211\u4eec\u5c31\u9700\u8981\u91c7\u7528\u4e00\u4e9b\u65b9\u6cd5\u903c\u8fd1\u8fd9\u4e2a\u95ee\u9898\u7684\u89e3\u3002\u8fd9\u4e5f\u662f\u4e0d\u540c\u7a00\u758f\u4e3b\u6210\u5206\u5206\u6790\u7b97\u6cd5\u4e0d\u540c\u7684\u7531\u6765\u3002<\/p>\n\n\n\n NMF\u7684\u57fa\u672c\u601d\u60f3\u53ef\u4ee5\u7b80\u5355\u63cf\u8ff0\u4e3a\uff1a\u5bf9\u4e8e\u4efb\u610f\u7ed9\u5b9a\u7684\u4e00\u4e2a\u975e\u8d1f\u77e9\u9635V\uff0cNMF\u7b97\u6cd5\u80fd\u591f\u5bfb\u627e\u5230\u4e00\u4e2a\u975e\u8d1f\u77e9\u9635W\u548c\u4e00\u4e2a\u975e\u8d1f\u77e9\u9635H\uff0c\u4f7f\u5f97\u6ee1\u8db3 \uff0c\u4ece\u800c\u5c06\u4e00\u4e2a\u975e\u8d1f\u7684\u77e9\u9635\u5206\u89e3\u4e3a\u5de6\u53f3\u4e24\u4e2a\u975e\u8d1f\u77e9\u9635\u7684\u4e58\u79ef\u3002\u5982\u4e0b\u56fe\u6240\u793a\uff0c\u5176\u4e2d\u8981\u6c42\u5206\u89e3\u540e\u7684\u77e9\u9635H\u548cW\u90fd\u5fc5\u987b\u662f\u975e\u8d1f\u77e9\u9635\u3002<\/p>\n\n\n\n \u53c2\u8003\uff1a<\/p>\n\n\n\n https:\/\/zhuanlan.zhihu.com\/p\/142143151<\/a><\/p>\n\n\n\n https:\/\/github.com\/jake-g\/dsp-fpga-labs\/blob\/c0c84ed08c02da11fb4161599cafe8ddcabeaca4\/Labs\/sound_split_demo\/seperate.m<\/a><\/p>\n\n\n\n https:\/\/www.cxymm.net\/article\/weixin_30446557\/115847250<\/a><\/p>\n\n\n\n https:\/\/leoncuhk.gitbooks.io\/feature-engineering\/content\/feature-extracting04.html<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" \u5728\u65af\u5766\u798f\u5927\u5b66\u7684Coursera\u7684Andrew Ng\u7684\u673a\u5668\u5b66\u4e60\u4ecb\u7ecd\u6027\u8bb2\u5ea7\u4e2d\uff0c\u4ed6\u7ed9\u51fa\u4e86\u4ee5\u4e0b\u4e00\u884cOctave\u89e3\u51b3\u65b9\u6848 … \u7ee7\u7eed\u9605\u8bfb\u9e21\u5c3e\u9152\u4f1a\u95ee\u9898—\u8bed\u97f3\u5206\u79bb<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4,12],"tags":[],"_links":{"self":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/560"}],"collection":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/comments?post=560"}],"version-history":[{"count":14,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/560\/revisions"}],"predecessor-version":[{"id":676,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/560\/revisions\/676"}],"wp:attachment":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/media?parent=560"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/categories?post=560"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/tags?post=560"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"\/](\"https:\/\/pic1.zhimg.com\/v2-9f38b91032b0238f9e016abf125d8210_r.jpg\")
<\/figure>\n\n\n\n
\u2003\u2003\u72ec\u7acb\u6210\u5206\u5206\u6790\u662f\u4ece\u76f2\u6e90\u5206\u79bb\u6280\u672f\u53d1\u5c55\u800c\u6765\u7684\u4e00\u79cd\u6570\u636e\u9a71\u52a8\u7684\u4fe1\u53f7\u5904\u7406\u65b9\u6cd5, \u662f\u57fa\u4e8e\u9ad8\u9636\u7edf\u8ba1\u7279\u6027\u7684\u5206\u6790\u65b9\u6cd5\u3002\u5b83\u5229\u7528\u7edf\u8ba1\u539f\u7406\u8fdb\u884c\u8ba1\u7b97\uff0c\u901a\u8fc7\u7ebf\u6027\u53d8\u6362\u628a\u6570\u636e\u6216\u4fe1\u53f7\u5206\u79bb\u6210\u7edf\u8ba1\u72ec\u7acb\u7684\u975e\u9ad8\u65af\u7684\u4fe1\u53f7\u6e90\u7684\u7ebf\u6027\u7ec4\u5408\u3002
\u2003\u2003\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u662f\u4e00\u79cd\u6570\u636e\u964d\u7ef4\u7684\u65b9\u6cd5\uff0c\u5b83\u4e0e\u4e3b\u6210\u5206\u5206\u6790\u6709\u4e00\u4e9b\u533a\u522b.<\/p>\n\n\n\n#coding:utf-8\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import signal\nfrom sklearn.decomposition import FastICA, PCA\n# \u751f\u6210\u89c2\u6d4b\u6a21\u62df\u6570\u636e\nnp.random.seed(0)\nn_samples = 2000\ntime = np.linspace(0, 8, n_samples)\ns1 = np.sin(2 * time) # \u4fe1\u53f7\u6e90 1 : \u6b63\u5f26\u4fe1\u53f7\ns2 = np.sign(np.sin(3 * time)) # \u4fe1\u53f7\u6e90 2 : \u65b9\u5f62\u4fe1\u53f7\ns3 = signal.sawtooth(2 * np.pi * time) # \u4fe1\u53f7\u6e90 3: \u952f\u9f7f\u6ce2\u4fe1\u53f7\nS = np.c_[s1, s2, s3]\nS += 0.2 * np.random.normal(size=S.shape) # \u589e\u52a0\u566a\u97f3\u6570\u636e\nS \/= S.std(axis=0) # \u6807\u51c6\u5316\n\n# \u6df7\u5408\u6570\u636e\nA = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # \u6df7\u5408\u77e9\u9635\nX = np.dot(S, A.T) # \u751f\u6210\u89c2\u6d4b\u4fe1\u53f7\u6e90\n\n# ICA\u6a21\u578b\nica = FastICA(n_components=3)\nS_ = ica.fit_transform(X) # \u91cd\u6784\u4fe1\u53f7\nA_ = ica.mixing_ # \u83b7\u5f97\u4f30\u8ba1\u6df7\u5408\u540e\u7684\u77e9\u9635\n\n# PCA\u6a21\u578b\npca = PCA(n_components=3)\nH = pca.fit_transform(X) # \u57fa\u4e8ePCA\u7684\u6210\u5206\u6b63\u4ea4\u91cd\u6784\u4fe1\u53f7\u6e90\n\n# \u56fe\u5f62\u5c55\u793a\nplt.figure()\nmodels = [X, S, S_, H]\nnames = ['Observations (mixed signal)',\n 'True Sources',\n 'ICA recovered signals',\n 'PCA recovered signals']\ncolors = ['red', 'steelblue', 'orange']\nfor ii, (model, name) in enumerate(zip(models, names), 1):\n plt.subplot(4, 1, ii)\n plt.title(name)\n for sig, color in zip(model.T, colors):\n plt.plot(sig, color=color)\nplt.subplots_adjust(0, 0.1, 1.2, 1.5, 0.26, 0.46)\nplt.show()\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n
ica = FastICA(n_components=None, algorithm=’parallel’, whiten=True, fun=’logcosh’, fun_args=None, max_iter=200, w_init=None)<\/p>\n\n\n\n\u7a00\u758f\u4e3b\u6210\u5206\u5206\u6790Spars PCA<\/h3>\n\n\n\n
\u975e\u8d1f\u77e9\u9635\u5206\u89e3NMF<\/h3>\n\n\n\n