Marker selection for human activity recognition using combination of conformal geometric algebra and principal component regression

Deep Learning (DL) plays an important role in machine learning and artificial intelligence. DL is widely applied in many fields with high dimensional data, including natural language processing, image recognition. High dimensional data can lead to problems in machine learning such as overfitting, degradation of accuracy. To address these issues, some methods, such as Principal Components Analysis (PCA), principal component regression (PCR), Multi-class Linear Discriminant Analysis (MLDA), were proposed to reduce dimensions of the data and computational complexity simultaneously. The drawback of these methods is that they only work well on data distributed on the plane. In the case of the data distributed on the hyper-sphere, such as objects moving in space, the processing results are not so good as expected. In this paper, we propose the use of Conformal Geometric Algebra (CGA) to extract features and simultaneously reduce the dimensionality of a dataset for human activity recognition using Recurrent Neural Network (RNN). First, human activity data in a 3-dimensional coordinate system is pre-processed and normalized by calculating deviations from the mean coordinate. Next, the data is transformed to vectors in CGA space and its dimensions are reduced to return the feature vectors. Finally, we use the RNN model to train feature vectors. Empirical results performed on the CMU eight actions dataset show that the CGA combined with RNN gave the best test results of 92.5%.

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“…In the previous study [26], we used PCA and CGA to extraction feature and predict on data set [25]. The best result is 88.86 %.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

“…(25) and with the proposed transformation using Eq. (26). The parameters of the RNN network are the number of neural  200, epochs  20, and classes  8 (8 kinds of human action), batch_size  5, and activation function is Tanh.…”

Section: Predict With Rnnmentioning

confidence: 99%