Cholesky decomposition failed when training plda

Hello,
When I train plda using some feature I extracted, this error occured.The tail of log is shown below
```
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 146608
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 209565
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 5 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 140.852
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 197.8
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 6 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 105.141
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 157.448
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 7 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 1117.95
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 5858.14
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 8 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 140.276
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 395.243
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 9 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 12926.4
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 12056.3
LOG (ivector-compute-plda[5.5.0~3-327d]:GetOutput():plda.cc:540) Norm of mean of iVector distribution is 0.745405
WARNING (ivector-compute-plda[5.5.0~3-327d]:Cholesky():tp-matrix.cc:110) Cholesky decomposition failed. Maybe matrix is not positive definite. Throwing error
Cholesky decomposition failed.# Accounting: begin_time=1558008300

And when I add some very small random noise to my feature like you said in the LDA computing when facing the same problem, the error was still there.
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 7 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 101755
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 141957
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 8 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 3136.84
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 17124.3
LOG (ivector-compute-plda[5.5.0~3-327d]:Estimate():plda.cc:529) Plda estimation iteration 9 of 10
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:511) Trace of within-class variance is 1.78053e+08
LOG (ivector-compute-plda[5.5.0~3-327d]:EstimateFromStats():plda.cc:512) Trace of between-class variance is 1.22304e+08
LOG (ivector-compute-plda[5.5.0~3-327d]:GetOutput():plda.cc:540) Norm of mean of iVector distribution is 0.745405
WARNING (ivector-compute-plda[5.5.0~3-327d]:Cholesky():tp-matrix.cc:110) Cholesky decomposition failed. Maybe matrix is not positive definite. Throwing error
Cholesky decomposition failed.# Accounting: begin_time=1558007075
```

Start with the ivector-compute-plda path in plda.cc, especially EstimateFromStats(), Estimate(), and GetOutput(), then inspect the Cholesky failure reported from tp-matrix.cc:110. Reproduce the failure with the feature data and logs described in the issue; the issue does not define an expected fix, so done would require identifying the cause and agreeing on the required behavior.

Written by the indexing model from the issue text.