Dimensionality reduction for stationary time series via stochastic nonconvex optimization

Keyphrases

• Dimensionality Reduction 100%
• Time Series Data 100%
• Stationary Time Series 100%
• Non-convex Stochastic Optimization 100%
• Machine Learning 50%
• Numerical Experiments 50%
• Stationary Distribution 50%
• Near-optimal 50%
• Non-convexity 50%
• Data Dependency 50%
• Fundamental Challenges 50%
• Stochastic Algorithm 50%
• Diffusion Approximation 50%
• Covariance Matrix 50%
• Downsampling 50%
• Stochastic Optimization 50%
• Stochastic Gradient 50%
• Oja's Algorithm 50%
• Streaming PCA 50%
• Algorithm-based 50%
• Provable Guarantees 50%
• Sample Complexity 50%
• Asymptotic Convergence Rates 50%

Mathematics

• Stochastics 100%
• Dimensionality Reduction 100%
• Stationary Time Series 100%
• Asymptotics 50%
• Time Series Data 50%
• Numerical Experiment 25%
• Data Point 25%
• Objective Function 25%
• Covariance Matrix 25%
• Diffusion Approximation 25%
• Principal Component Analysis 25%
• Rate of Convergence 25%