In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative sub-space learning framework and derive novel multiplicative up-date rules for its optimization. The usefulness of the devel-oped algorithm is demonstrated for unsupervised facial be-haviour dynamics analysis on MMI database
In this paper we propose a non-negative matrix factorization (NMF) model with piecewise-constant act...
Slow feature analysis is an algorithm for unsupervised learning of invariant representations from da...
Slow feature analysis (SFA) is motivated by biological model to extracts slowly varying feature from...
In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning i...
A recently introduced latent feature learning technique for time varying dynamic phenomena analysis ...
The analysis of high-dimensional, possibly temporally misaligned, and time-varying visual data is a ...
Abstract — A recently introduced latent feature learning technique for time-varying dynamic phenomen...
A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis ...
Human facial behaviour analysis is an important task in developing automatic Human-Computer Interact...
Approximate nonnegative matrix factorization (NMF) is an emerging technique with a wide spectrum of ...
Temporal networks describe a large variety of systems having a temporal evolution. Characterization ...
International audienceTemporal networks describe a large variety of systems having a temporal evolut...
The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-neg...
Nonnegative matrix factorization is a linear dimensionality reduction technique used for decomposing...
Without non-linear basis functions many problems can not be solved by linear algorithms. This articl...
In this paper we propose a non-negative matrix factorization (NMF) model with piecewise-constant act...
Slow feature analysis is an algorithm for unsupervised learning of invariant representations from da...
Slow feature analysis (SFA) is motivated by biological model to extracts slowly varying feature from...
In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning i...
A recently introduced latent feature learning technique for time varying dynamic phenomena analysis ...
The analysis of high-dimensional, possibly temporally misaligned, and time-varying visual data is a ...
Abstract — A recently introduced latent feature learning technique for time-varying dynamic phenomen...
A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis ...
Human facial behaviour analysis is an important task in developing automatic Human-Computer Interact...
Approximate nonnegative matrix factorization (NMF) is an emerging technique with a wide spectrum of ...
Temporal networks describe a large variety of systems having a temporal evolution. Characterization ...
International audienceTemporal networks describe a large variety of systems having a temporal evolut...
The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-neg...
Nonnegative matrix factorization is a linear dimensionality reduction technique used for decomposing...
Without non-linear basis functions many problems can not be solved by linear algorithms. This articl...
In this paper we propose a non-negative matrix factorization (NMF) model with piecewise-constant act...
Slow feature analysis is an algorithm for unsupervised learning of invariant representations from da...
Slow feature analysis (SFA) is motivated by biological model to extracts slowly varying feature from...
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