Add to ORKG• Slow feature analysis (SFA): an unsupervised learning technique for feature extraction from sequences driven by the slowness objective. Kompella et al. developed an Incremental SFA (IncSFA). • IncSFA has linear time complexity w.r.t. input dimension, while Batch SFA (BSFA) is cubic. BSFA has quadratic space complexity, IncSFA is no worse than quadratic. • IncSFA might suit autonomous agents: 1. advantageous computational complexity in the case of limited onboard hardware, and 2. it allows updating of existing slow features with new sensory input (i.e., if the environment changes) without any input storage. • Hierarchical SFA (H-SFA) suits images. Via multiple layers of feature extraction within small receptive fields, it takes advantage o...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis ...
Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]...
The Slow Feature Analysis (SFA) unsupervised learning framework extracts features representing the u...
the date of receipt and acceptance should be inserted later Abstract Slow Feature Analysis (SFA) is ...
High dimensional input streams and unsupervised learning are two important factors in the area of hu...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
A recently introduced latent feature learning technique for time varying dynamic phenomena analysis ...
The appearance of objects in an image can change dramatically depending on their pose, distance, and...
Slow feature analysis (SFA) is motivated by biological model to extracts slowly varying feature from...
Abstract—Slow Feature Analysis (SFA) is a feature extraction algorithm based on the slowness princip...
The paper presents an agent-based framework for in-vestigating a class of learning algorithms that e...
The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal o...
In dieser Doktorarbeit werden neue Erweiterungen der "Slow Feature Analysis" (SFA) zur effizienten u...
We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range ...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis ...
Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]...
The Slow Feature Analysis (SFA) unsupervised learning framework extracts features representing the u...
the date of receipt and acceptance should be inserted later Abstract Slow Feature Analysis (SFA) is ...
High dimensional input streams and unsupervised learning are two important factors in the area of hu...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
A recently introduced latent feature learning technique for time varying dynamic phenomena analysis ...
The appearance of objects in an image can change dramatically depending on their pose, distance, and...
Slow feature analysis (SFA) is motivated by biological model to extracts slowly varying feature from...
Abstract—Slow Feature Analysis (SFA) is a feature extraction algorithm based on the slowness princip...
The paper presents an agent-based framework for in-vestigating a class of learning algorithms that e...
The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal o...
In dieser Doktorarbeit werden neue Erweiterungen der "Slow Feature Analysis" (SFA) zur effizienten u...
We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range ...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis ...
Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]...