Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Probabilistic Finite Automaton (PFA), Probabilistic Finite State Transducers (PFST) and Hidden Markov Models (HMM) are widely used in Automatic Speech Recognition (ASR), Text-to-Speech (TTS) systems and Part Of Speech (POS) tagging for language modeling. Traditionally, unsupervised learning of these latent variable models is done by Expectation-Maximization (EM)-like algorithms, as the Baum-lch algorithm. In a recent alternative line of work, learning algorithms based on spectral properties of some low order moments matrices or tensors re proposed. In comparison to EM, they are orders of magnitude faster and come with theoretical convergence guarantees. Hover, returned models are not ensured to compute proper distributions. They often return negative values that do not sum to one, limiting their applicability and preventing them to serve as an initialization to EM-like algorithms. ,  propose a new spectral algorithm able to learn a large range of models constrained to return proper distributions.  assess its performances on synthetic problems from the PAutomaC challenge and real datasets extracted from Wikipedia. Experiments show that it outperforms previous spectral approaches as ll as the Baum-lch algorithm with random restarts, in addition to serve as an efficient initialization step to EM-like algorithms.
['Hadrien Glaude', 'Cyrille Enderli', 'Olivier Pietquin']
I am writing a research paper on a idea Spectral learning with non negative probabilities for finite state automaton
[{"from": "human", "value": "I am writing a research paper on a idea Spectral learning with non negative probabilities for finite state automaton"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Probabilistic Finite Automaton (PFA), Probabilistic Finite State Transducers (PFST) and Hidden Markov Models (HMM) are widely used in Automatic Speech Recognition (ASR), Text-to-Speech (TTS) systems and Part Of Speech (POS) tagging for language modeling. Traditionally, unsupervised learning of these latent variable models is done by Expectation-Maximization (EM)-like algorithms, as the Baum-lch algorithm. In a recent alternative line of work, learning algorithms based on spectral properties of some low order moments matrices or tensors re proposed. In comparison to EM, they are orders of magnitude faster and come with theoretical convergence guarantees. Hover, returned models are not ensured to compute proper distributions. They often return negative values that do not sum to one, limiting their applicability and preventing them to serve as an initialization to EM-like algorithms. ,  propose a new spectral algorithm able to learn a large range of models constrained to return proper distributions.  assess its performances on synthetic problems from the PAutomaC challenge and real datasets extracted from Wikipedia. Experiments show that it outperforms previous spectral approaches as ll as the Baum-lch algorithm with random restarts, in addition to serve as an efficient initialization step to EM-like algorithms."}]