Nonlinear dimensionality reduction using a temporal coherence principle
Temporal coherence principle is an attractive biologically inspired learning rule to extract slowly varying features from quickly varying input data. In this paper we develop a new Nonlinear Neighborhood Preserving (NNP) technique, by utilizing the temporal coherence principle to find an optimal low dimensional representation from the original high dimensional data. NNP is based on a nonlinear expansion of the original input data, such as polynomials of a given degree. It can be solved by the eigenvalue problem without using gradient descent and is guaranteed to find the global optimum. NNP can be viewed as a nonlinear dimensionality reduction framework which takes into consideration both time series and data sets without an obvious temporal structure. According to different situations, we introduce three algorithms of NNP, named NNP-1, NNP-2, and NNP-3. The objective function of NNP-1 is equal to Slow Feature Analysis (SFA), and it works well for time series such as image sequences. NNP-2 artificially constructs time series consisting of neighboring points for data sets without a clear temporal structure such as image data. NNP-3 is proposed for classification tasks, which can minimize the distances of neighboring points in the embedding space and ensure that the remaining points are as far apart as possible simultaneously. Furthermore, the kernel extension of NNP is also discussed in this paper. The proposed algorithms work very well on some image sequences and image data sets compared to other methods. Meanwhile, we perform the classification task on the MNIST handwritten digit database using the supervised NNP algorithms. The experimental results demonstrate that NNP is an effective technique for nonlinear dimensionality reduction tasks.
dimensionality reduction temporal coherence principle feature extraction temporal slowness principle manifold learning pattern classification character recognition