6,349 research outputs found
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Stochastic optimization naturally arises in machine learning. Efficient
algorithms with provable guarantees, however, are still largely missing, when
the objective function is nonconvex and the data points are dependent. This
paper studies this fundamental challenge through a streaming PCA problem for
stationary time series data. Specifically, our goal is to estimate the
principle component of time series data with respect to the covariance matrix
of the stationary distribution. Computationally, we propose a variant of Oja's
algorithm combined with downsampling to control the bias of the stochastic
gradient caused by the data dependency. Theoretically, we quantify the
uncertainty of our proposed stochastic algorithm based on diffusion
approximations. This allows us to prove the asymptotic rate of convergence and
further implies near optimal asymptotic sample complexity. Numerical
experiments are provided to support our analysis
Design and implementation of an intelligent car obstacle avoidance system based on deep learning
Through the integration of deep learning technology, from the simplest driving method to the realizatio n of the “carnetwork road” interaction, the use of STM32F103 microprocessor control chip, and through the PWM technology to achieve the speed and
steering gear regulation, at the same time, the use of deep learning self-cognition technology, so that intelligent vehicles can make selfcognitive decisions like human minds , by looking for the best route to avoid some obstacles on the road surface, and the selection of the
optimal forecast route, and through the tracking controller to achieve the black line function, through the anti-collision system to achieve the
vehicle detection and obstacle avoidance function
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