Lots unsupervised deep learning methods for multivariate time series data have AE or GAN based. My question is that even though these algorithms are for unsupervised learning, the training only use normal data. Is there any solution to train datasets that has no label(cannot identify which dataset is normal or abnormal) to these networks?
Lets say I have Neural Network (NN) that is trained to recognize cats given an image, is there a way to update my NN to recognize dogs as well?
More generally, my question is regarding a way to extend a NN by kind a "loading patterns library".
This is generally known as transfer learning, you basically train a neural network on a large dataset (like ImageNet) and then use the feature vector that is generated by the final convolutional layer to train another classifier (a multiclass SVM for example), and this works even if the objects are different.
Another way is to take a pretrained network and retrain the classifier part (the fully connected layers). It is still faster than training a network from scratch.
Currently for face detection I am using svm classifier over HOG feature set.But I need to implement other classifiers over those HOG feature set and compare the results between them .What other classifiers can I use other than svm?
There are plenty of other classification algorithms, a simple logistic regression could be a starting point. You could use logistic regression; implement gaussian process based classifier or random forrests/decision trees and many others with respective pro's and cons.
See e.g. this link for an overview
I just want to know if a neural network can be trained with a single class of data set. I have a set of data that I want to train a neural network with. After training it, I want to give new data(for testing) to the trained neural network to check if it can recognize it as been similar to the training sample or not.
Is this possible with neural network? If yes, will that be a supervised learning or unsupervised.
I know neural networks can be used for classification if there are multiple classes but I have not seen with a single class before. A good explanation and link to any example will be much appreciated. Thanks
Of course it can be. But in this case it will only recognize this one class that you have trained it with. And depending on the expected output you can measure the similarity to the training data.
An NN, after training, is just a function. For classification problems you can imagine it as a function that takes data as input and returns an integer indicating to which class it belongs to. That being said, if you have only one class that can be represented by an integer value 1, and if training data is not similar to that class, you will get something like 1.555; It will not tel you that it belongs to another class, because you have introduced only one, but it will definitely give you a hint about its similarity.
NNs are considered to be supervised learning, because before training you have to provide both input and target, i. e. the expected output.
If you train a network with only a single class of data then It is popularly known as One-class Classification. There are various algorithms developed in the past like One-class SVM, Support Vector Data Description, OCKELM etc. Tax and Duin developed a MATLAB toolbox for this and it supports various one-class classifiers.
DD Toolbox
One-class SVM
Kernel Ridge Regression based or Kernelized ELM based or LSSVM(where bias=0) based One-class Classification
There is a paper Anomaly Detection Using One-Class Neural Networks
which combines One-Class SVM and Neural Networks.
Here is source code. However, I've had difficulty connecting the source code and the paper.
Are there any algorithms that quantify the similarity of tree based models such as XGB? For example, I train two XGB models with different datasets for example in cross validation and want to estimate the robustness or consistency of the predictions and maybe how features are used.