Package smile.feature.extraction
The main linear technique for dimensionality reduction, principal component analysis, performs a linear mapping of the data to a lower dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. In practice, the correlation matrix of the data is constructed and the eigenvectors on this matrix are computed. The eigenvectors that correspond to the largest eigenvalues (the principal components) can now be used to reconstruct a large fraction of the variance of the original data. Moreover, the first few eigenvectors can often be interpreted in terms of the large-scale physical behavior of the system. The original space has been reduced (with data loss, but hopefully retaining the most important variance) to the space spanned by a few eigenvectors.
Compared to regular batch PCA algorithm, the generalized Hebbian algorithm is an adaptive method to find the largest k eigenvectors of the covariance matrix, assuming that the associated eigenvalues are distinct. GHA works with an arbitrarily large sample size and the storage requirement is modest. Another attractive feature is that, in a non-stationary environment, it has an inherent ability to track gradual changes in the optimal solution in an inexpensive way.
Random projection is a promising linear dimensionality reduction technique for learning mixtures of Gaussians. The key idea of random projection arises from the Johnson-Lindenstrauss lemma: if points in a vector space are projected onto a randomly selected subspace of suitably high dimension, then the distances between the points are approximately preserved.
Principal component analysis can be employed in a nonlinear way by means of the kernel trick. The resulting technique is capable of constructing nonlinear mappings that maximize the variance in the data. The resulting technique is entitled Kernel PCA. Other prominent nonlinear techniques include manifold learning techniques such as locally linear embedding (LLE), Hessian LLE, Laplacian eigenmaps, and LTSA. These techniques construct a low-dimensional data representation using a cost function that retains local properties of the data, and can be viewed as defining a graph-based kernel for Kernel PCA. More recently, techniques have been proposed that, instead of defining a fixed kernel, try to learn the kernel using semidefinite programming. The most prominent example of such a technique is maximum variance unfolding (MVU). The central idea of MVU is to exactly preserve all pairwise distances between nearest neighbors (in the inner product space), while maximizing the distances between points that are not nearest neighbors.
An alternative approach to neighborhood preservation is through the minimization of a cost function that measures differences between distances in the input and output spaces. Important examples of such techniques include classical multidimensional scaling (which is identical to PCA), Isomap (which uses geodesic distances in the data space), diffusion maps (which uses diffusion distances in the data space), t-SNE (which minimizes the divergence between distributions over pairs of points), and curvilinear component analysis.
A different approach to nonlinear dimensionality reduction is through the use of autoencoders, a special kind of feed-forward neural networks with a bottleneck hidden layer. The training of deep encoders is typically performed using a greedy layer-wise pre-training (e.g., using a stack of Restricted Boltzmann machines) that is followed by a fine-tuning stage based on backpropagation.
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ClassesClassDescriptionThe bag-of-words feature of text used in natural language processing and information retrieval.Encodes categorical features using sparse one-hot scheme.Generalized Hebbian Algorithm.Feature hashing, also known as the hashing trick, is a fast and space-efficient way of vectorizing features, i.e.Kernel PCA transform.Principal component analysis.Probabilistic principal component analysis.A projection is a kind of feature extraction technique that transforms data from the input space to a feature space, linearly or non-linearly.Random projection is a promising dimensionality reduction technique for learning mixtures of Gaussians.Encodes numeric and categorical features into sparse array with on-hot encoding of categorical variables.