1.14. Semi-supervised learning¶
Semi-supervised learning is a situation
in which in your training data some of the samples are not labeled. The
semi-supervised estimators in
sklearn.semi_supervised are able to
make use of this additional unlabeled data to better capture the shape of
the underlying data distribution and generalize better to new samples.
These algorithms can perform well when we have a very small amount of
labeled points and a large amount of unlabeled points.
Unlabeled entries in
It is important to assign an identifier to unlabeled points along with the
labeled data when training the model with the
fit method. The
identifier that this implementation uses is the integer value \(-1\).
Note that for string labels, the dtype of
y should be object so that it
can contain both strings and integers.
Semi-supervised algorithms need to make assumptions about the distribution of the dataset in order to achieve performance gains. See here for more details.
1.14.1. Self Training¶
This self-training implementation is based on Yarowsky’s 1 algorithm. Using this algorithm, a given supervised classifier can function as a semi-supervised classifier, allowing it to learn from unlabeled data.
SelfTrainingClassifier can be called with any classifier that
predict_proba, passed as the parameter
each iteration, the
base_classifier predicts labels for the unlabeled
samples and adds a subset of these labels to the labeled dataset.
The choice of this subset is determined by the selection criterion. This
selection can be done using a
threshold on the prediction probabilities, or
by choosing the
k_best samples according to the prediction probabilities.
The labels used for the final fit as well as the iteration in which each sample
was labeled are available as attributes. The optional
specifies how many times the loop is executed at most.
max_iter parameter may be set to
None, causing the algorithm to iterate
until all samples have labels or no new samples are selected in that iteration.
When using the self-training classifier, the calibration of the classifier is important.
“Unsupervised word sense disambiguation rivaling supervised methods” David Yarowsky, Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL ‘95). Association for Computational Linguistics, Stroudsburg, PA, USA, 189-196.
1.14.2. Label Propagation¶
Label propagation denotes a few variations of semi-supervised graph inference algorithms.
- A few features available in this model:
Used for classification tasks
Kernel methods to project data into alternate dimensional spaces
differ in modifications to the similarity matrix that graph and the
clamping effect on the label distributions.
Clamping allows the algorithm to change the weight of the true ground labeled
data to some degree. The
LabelPropagation algorithm performs hard
clamping of input labels, which means \(\alpha=0\). This clamping factor
can be relaxed, to say \(\alpha=0.2\), which means that we will always
retain 80 percent of our original label distribution, but the algorithm gets to
change its confidence of the distribution within 20 percent.
LabelPropagation uses the raw similarity matrix constructed from
the data with no modifications. In contrast,
minimizes a loss function that has regularization properties, as such it
is often more robust to noise. The algorithm iterates on a modified
version of the original graph and normalizes the edge weights by
computing the normalized graph Laplacian matrix. This procedure is also
used in Spectral clustering.
Label propagation models have two built-in kernel methods. Choice of kernel effects both scalability and performance of the algorithms. The following are available:
rbf (\(\exp(-\gamma |x-y|^2), \gamma > 0\)). \(\gamma\) is specified by keyword gamma.
knn (\(1[x' \in kNN(x)]\)). \(k\) is specified by keyword n_neighbors.
The RBF kernel will produce a fully connected graph which is represented in memory by a dense matrix. This matrix may be very large and combined with the cost of performing a full matrix multiplication calculation for each iteration of the algorithm can lead to prohibitively long running times. On the other hand, the KNN kernel will produce a much more memory-friendly sparse matrix which can drastically reduce running times.
 Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised Learning (2006), pp. 193-216
 Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005 https://research.microsoft.com/en-us/people/nicolasl/efficient_ssl.pdf