unsupervised anomaly detection on sparse data
1- You better start with Isolation forest Isolation Forest This is a very simple algorithm where you can control the contamination rate of your data.
2- For visualization you can plot the anomalous points in red, and you can distinguish them using the Isolation Forest predict(X) function that returns -1 for outliers and 1 for the rest.
3- You can use LSTM by comparing the predicted values of your model, with the real values of your dataset, then use a KPI ( if/else depending on the max value of the difference between the predicted values and the real ones), in simple words you will need to define your own contamination rate.
4- For visualization with LSTM you can use the same function described above with Isolation Forest, but instead of the predict function that return [-1,1], you will use the function described in step 4.