Hence, at each iteration, less and less human intervention is required to correct labels. At each new iteration, the network is trained with more and more data, causing the prediction and labeling performance of the network to improve. This latter task requires considerably less human labeling effort. However, the task changes from labeling signals from scratch to correcting inaccurate labels generated by a reliable network. The validated labeled signals are added to a training data set to retrain the deep network with the extended training data.Īt each iteration, the human labeler still has to visit and examine all the signals labeled by the network. A human labeler examines the resulting labels and corrects wrong labels. At each iteration, a subset of signals is selected from the unlabeled data set and is sent to a pretrained deep network for automated labeling. An alternative approach, explored in this example, treats the labeling process iteratively. This approach requires much time and effort. A first approach consists of labeling all the data by hand. Finding ways to reduce this effort can significantly speed up the development of deep learning solutions for signal processing problems.Ĭonsider the task of labeling regions of interest in a signal data set. Labeling signal data is a tedious and expensive task that requires much human effort. In this case, both the features and the outcomes for the unknown samples are not available when the model would be used in practice, so they should not be available when training the model.This example presents an iterative deep learning-based workflow to label signals with reduced human labeling effort. Since in this case we won't know what the new features might look like and we can't rebuild the model to account for the new features, doing PCA on the testing data would be "peaking". If, on the other hand, you have a data set where you have to build the model now and at some point in the future you will get new samples that you have to predict using that prebuilt model, you must do separate PCA in each fold to be sure it will generalize. Since PCA is unsupervised, it isn't "peaking" because you can do the same thing to the unknown samples as you can to the known. If you have a dataset where you have a bunch of samples some of which are known and some are unknown and you want to predict the unknowns, including the unknowns in the PCA will give you are richer view of data diversity and can help improve the performance of the model. PCA can be done on the whole data set so long as you don't need to build your model in advance of knowing the data you are trying to predict. The answer to this question depends on your experimental design. If between-class variance is small, this bias won't be much, but in that case neither would PCA help for the classification: the PCA projection then cannot help emphasizing the separation between the classes. That is, in a situation where additional cases do influence the model. Usually the PCA step is done because you need to stabilize the classification. And if the between-class variance is large compared to the within-class variance, between-class variance will influence the PCA projection. I am still finding it difficult to get a feeling of how an initial PCA on the whole dataset would bias the results without seeing the class labels.īut it does see the data. Just make sure you keep the two approaches separate from each other. That being said, you can build an additional PCA model of the whole data set for descriptive (e.g. I once measured the bias of not repeating the PCA, and found that with my spectroscopic classification data, I detected only half of the generalization error rate when not redoing the PCA for every surrogate model. I think the result of this test is worth reporting. You can also test afterwards whether redoing the PCA for every surrogate model was necessary (repeating the analysis with only one PCA model). If you have an expectation how many PCs would be good from experience, you can maybe just use that. You'll need to define an automatic criterium for the number of PCs to use.Īs it is just a first data reduction step before the "actual" classification, using a few too many PCs will likely not hurt the performance. Then you project the data onto the PCs of the training set. you do not do a separate PCA on the test set! You subtract the mean (and if needed divide by the standard deviation) of the training set, as explained here: Zero-centering the testing set after PCA on the training set. You then apply the same transformation to the test set: i.e. For measuring the generalization error, you need to do the latter: a separate PCA for every training set (which would mean doing a separate PCA for every classifier and for every CV fold).
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