Add to ORKGThe Slow Feature Analysis (SFA) unsupervised learning framework extracts features representing the underlying causes of the changes within a tem-porally coherent high-dimensional raw sensory in-put signal. We develop the first online version of SFA, via a combination of incremental Princi-pal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, on-line SFA adapts along with non-stationary environ-ments, which makes it a generally useful unsuper-vised preprocessor for autonomous learning agents. We compare online SFA to batch SFA in several experiments and show that it indeed learns without a teacher to encode the input stream by informa-environmental properties. We extend online SFA to deep networks in hierarc...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
Abstract—At the core of vision research is the notion of perceptual invariance. The question of how ...
Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extra...
• Slow feature analysis (SFA): an unsupervised learning technique for feature extraction from sequen...
High dimensional input streams and unsupervised learning are two important factors in the area of hu...
the date of receipt and acceptance should be inserted later Abstract Slow Feature Analysis (SFA) is ...
Slow Feature Analysis (SFA) is a subspace learning method inspired by the human visual system, howev...
The paper presents an agent-based framework for in-vestigating a class of learning algorithms that e...
Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]...
A recently introduced latent feature learning technique for time varying dynamic phenomena analysis ...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
It is open how neurons in the brain are able to learn without supervision to discrim-inate between s...
Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how vis...
Slow feature analysis is an algorithm for unsupervised learning of invariant representations from da...
The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal o...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
Abstract—At the core of vision research is the notion of perceptual invariance. The question of how ...
Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extra...
• Slow feature analysis (SFA): an unsupervised learning technique for feature extraction from sequen...
High dimensional input streams and unsupervised learning are two important factors in the area of hu...
the date of receipt and acceptance should be inserted later Abstract Slow Feature Analysis (SFA) is ...
Slow Feature Analysis (SFA) is a subspace learning method inspired by the human visual system, howev...
The paper presents an agent-based framework for in-vestigating a class of learning algorithms that e...
Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]...
A recently introduced latent feature learning technique for time varying dynamic phenomena analysis ...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
It is open how neurons in the brain are able to learn without supervision to discrim-inate between s...
Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how vis...
Slow feature analysis is an algorithm for unsupervised learning of invariant representations from da...
The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal o...
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We pro...
Abstract—At the core of vision research is the notion of perceptual invariance. The question of how ...
Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extra...