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| author | Daniel Wolff |
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| date | Fri, 19 Aug 2016 13:07:06 +0200 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 Netlab Reference Manual gmminit | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> gmminit | |
| 9 </H1> | |
| 10 <h2> | |
| 11 Purpose | |
| 12 </h2> | |
| 13 Initialises Gaussian mixture model from data | |
| 14 | |
| 15 <p><h2> | |
| 16 Synopsis | |
| 17 </h2> | |
| 18 <PRE> | |
| 19 | |
| 20 mix = gmminit(mix, x, options) | |
| 21 </PRE> | |
| 22 | |
| 23 | |
| 24 <p><h2> | |
| 25 Description | |
| 26 </h2> | |
| 27 <CODE>mix = gmminit(mix, x, options)</CODE> uses a dataset <CODE>x</CODE> | |
| 28 to initialise the parameters of a Gaussian mixture | |
| 29 model defined by the data structure <CODE>mix</CODE>. The k-means algorithm | |
| 30 is used to determine the centres. The priors are computed from the | |
| 31 proportion of examples belonging to each cluster. | |
| 32 The covariance matrices are calculated as the sample covariance of the | |
| 33 points associated with (i.e. closest to) the corresponding centres. | |
| 34 For a mixture of PPCA model, the PPCA decomposition is calculated | |
| 35 for the points closest to a given centre. | |
| 36 This initialisation can be used as the starting point for training the | |
| 37 model using the EM algorithm. | |
| 38 | |
| 39 <p><h2> | |
| 40 Example | |
| 41 </h2> | |
| 42 <PRE> | |
| 43 | |
| 44 mix = gmm(3, 2); | |
| 45 options = foptions; | |
| 46 options(14) = 5; | |
| 47 mix = gmminit(mix, data, options); | |
| 48 </PRE> | |
| 49 | |
| 50 This code sets up a Gaussian mixture model with 3 centres in 2 dimensions, and | |
| 51 then initialises the parameters from the data set <CODE>data</CODE> with 5 iterations | |
| 52 of the k means algorithm. | |
| 53 | |
| 54 <p><h2> | |
| 55 See Also | |
| 56 </h2> | |
| 57 <CODE><a href="gmm.htm">gmm</a></CODE><hr> | |
| 58 <b>Pages:</b> | |
| 59 <a href="index.htm">Index</a> | |
| 60 <hr> | |
| 61 <p>Copyright (c) Ian T Nabney (1996-9) | |
| 62 | |
| 63 | |
| 64 </body> | |
| 65 </html> |