Learning the Structure of a Low-Rank Tensor with Partially-Latent Variables


Learning the Structure of a Low-Rank Tensor with Partially-Latent Variables – Recent studies suggest that many applications in the real world (e.g., image classification, speech recognition, and speech recognition) are dominated by non-stationary, non-linear, feature space models. This article focuses on a non-lattice-based approach to model continuous non-linear data. We provide a statistical study of stochastic noise, where a stochastic process is described by a manifold (analogy or a binary hierarchy) of non-stationary, non-linear components, and we describe a variational flow model for continuous non-linear data from a single stochastic process. Experimental results demonstrate that in fact, our variational flow model is useful both for predicting the presence of continuous non-linear data, and for modelling continuous data and data with Gaussian noise variables in noisy data streams.

We compare two techniques of general nonlinear dimensionality reduction when the search space is limited and the objective function is the least-squares problem. The two techniques are complementary: a simple technique for comparing the results of different methods, and an efficient technique for comparing the results of two techniques when the search space is limited. We show that the two techniques may be compared on a one-dimensional metric space, and present a new metric based on the metric’s minimorization. The metric is shown to produce positive results, negative results, and both techniques should be used together in practice. We show that the metric and the metric can be easily applied to perform the standard nonlinear dimensionality reduction (or the partial nonlinear dimensionality reduction) and, for that, to compute one-dimensional alternatives. We illustrate the use of the metric in both the search and optimization literature.

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Learning the Structure of a Low-Rank Tensor with Partially-Latent Variables

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  • Optical Flow Traces — A Computational Perspective

    A simple proof of general nonlinear dimensionality reductionWe compare two techniques of general nonlinear dimensionality reduction when the search space is limited and the objective function is the least-squares problem. The two techniques are complementary: a simple technique for comparing the results of different methods, and an efficient technique for comparing the results of two techniques when the search space is limited. We show that the two techniques may be compared on a one-dimensional metric space, and present a new metric based on the metric’s minimorization. The metric is shown to produce positive results, negative results, and both techniques should be used together in practice. We show that the metric and the metric can be easily applied to perform the standard nonlinear dimensionality reduction (or the partial nonlinear dimensionality reduction) and, for that, to compute one-dimensional alternatives. We illustrate the use of the metric in both the search and optimization literature.


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