We try to combine semi-supervised learning and multi-view learning together. This work is still ongoing.
We proposed a method which jointly explores the inter-instance feature distribution and intra-instance label relation simultaneously. We also applied co-training with pseudo labels to further improve learning performance. A relation network is further proposed to explore the cross-label relation knowledge.
We implemented a generative model to fully utilize the pair-wise information in different views. Our model can handle complete view, partial view and missing view at the same time. We also proposed a simple yet effective View Correlation Discovery Network to do the label-level fusion.
EMG stands for electromyography. It is the study of muscle electrical signals. Since EMG signal always contains noise, conventional methods use Root Mean Square(RMS) to reduce the noise and extract feature. However the RMS misses a lot of useful information. To reduce the dimension and avoid shifting in the signal, LDA and PCA are utilized. Traditional machine learning methods are implemented for the classification, including Random Forest, KNN, SVM. These methods lack a unified and principled objective function. Therefore, we proposed a method using Fast Fourier Transform(FFT) to extract the features. And then applied a LSTM model to do the classification. Our result is better than the previous traditional methods.
We analyzed several leaked password datasets and used machine learning to create a more general password dictionary.
I analyzed the relationship of topics and authors using some algorithms.
Traditional DTUs do not have bluetooth. To make it easier to use, we implemented a bluetooth part in it.
We proposed several methods to defend Evil Twin.