Multi-view vector processing method and multi-view vector processing device
A multi-view vector processing method and a multi-view vector processing device are provided. A multi-view vector x represents an object containing information on at least two non-discrete views. A model of the multi-view vector, where the model includes at least components of: a population mean μ of the multi-view vector, view component of each view of the multi-view vector and noise is established. The population mean μ, parameters of each view component and parameters of the noise , are obtained by using training data of the multi-view vector x. The device includes a processor and a storage medium storing program codes, and the program codes implements the aforementioned method when being executed by the processor.
1. A method of multi-view vector processing by a processor, where a multi-view vector x represents an object containing information on at least two non-discrete views, the method comprising:
establishing a model of the multi-view vector x, where the model includes at least components of: a population mean μ of the multi-view vector x, a view component of a view among the at least two non-discrete views of the multi-view vector x and noise ; and
using training data of the multi-view vector x to obtain the population mean μ, parameters of the view component and parameters of the noise ,
where,
the multi-view vector is obtained by processing a feature vector with a classifier, and the feature vector is obtained by directly vectorizing the object, and
the classifier is configured to relatively separate the multi-view vector from the feature vector obtained by directly vectorizing the object to be represented, and a discreteness between an excluded view and the two non-discrete views of the multi-view vector x is higher than a discreteness between the two non-discrete views of the multi-view vector x.
2. The method according to claim 1 , where the population mean μ, is set as zero.
3. The method according to claim 1 , where the view component of the view is based on a product of a space basis S i corresponding to the view and a coefficient u i —selected for the view, where i is a sequence number of the view.
4. The method according to claim 3 , where the noise is set to meet a Gauss distribution taking a diagonal matrix Σ as a covariance.
5. The method according to claim 4 , where to use the training data includes: obtaining the population mean μ, space base S n of the view and the Σ, based on the training data by using an expectation-maximization algorithm.
6. The method according to claim 5 , where in the expectation-maximization algorithm, mean expected values of a plurality of samples for the multi-view vector x with respect to the selected coefficient u i for the view component of the view, and expected values related to covariance with respect to the selected coefficient u i for the view component of the view, are calculatable based on μ, S n and Σ, and μ, S n and Σ are recalculatable until the mean expected values of the samples for the multi-view vector x and the expected values related to covariance converge.
7. The method according to claim 4 , where space bases of the at least two non-discrete views are respectively recorded as S and T, and the multi-view vector x is represented as
x ijk =μ+ Su i +Tv j +ϵ ijk
where μ represents the population mean, u i represents a coefficient corresponding to an i-th selection for the view corresponding to the space basis S, v j represents a coefficient corresponding to a j-th selection for the view corresponding to the space basis T, ϵ ijk represents the noise , and k represents a k-th sample under the i-th selection and the j-th selection.
8. The method according to claim 7 , where if θ={μ, S, T, Σ} and B=[S T], then the following distribution is met:
P ( x ijk |u i ,v j ,θ)= N ( x ijk |μ+Su i +Tv j ,Σ),
P ( u i )= N ( u i |0, I ),
P ( v j )= N ( v j |0, I ),
where N(x|μ, Σ) is a normal distribution with a mean of μ, and a variance of Σ, and | is a unit matrix.
9. The method according to claim 7 , where the multi-view vector x ijk represents a voiceprint for a k-th sample of a j-th type of text by an i-th speaker, u i is a coefficient of the i-th speaker and v j is a coefficient of the j-th type of text.
10. The method according to claim 1 , further including;
calculating a first likelihood representing that at least one view component is same among view components of at least two non-discrete views in two multi-view vectors and a second likelihood representing that the at least one view component is different among the view components of the at least two non-discrete views in the two multi-view vectors, by using population mean μ, parameters of a view component and parameters of noise of respective two multi-view vectors; and
determining whether the at least one view component is same in the two multi-view vectors based on the first and second likelihoods.
11. The method according to claim 10 , further including calculating a first probability representing that at least one view component is same among view components of at least two non-discrete views in the two multi-view vectors and a second probability representing that the at least one view component is different among the view components of the at least two non-discrete views in the two multi-view vectors based on the calculated first and second likelihoods, and determining whether the at least one view component is same in the two multi-view vectors based on the first and second probabilities.
12. The method according to claim 10 , further including: determining whether at least two of the view components are same among view components of at least two non-discrete views in the two multi-view vectors.
13. The method according to claim 8 , further including:
calculating a first likelihood representing that two view components both are same among view components of at least two non-discrete views in two multi-view vectors and a second likelihood representing that the two view components are different among the view components of the at least two non-discrete views in the two multi-view vectors based on the determined parameters of the multi-view vector model, and determining whether the two view components are both the same in the two multi-view vectors based on the first and second likelihoods, where
the first likelihood representing that a plurality of the view components are same,
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the second likelihood representing that at least one of the view components is different,
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where x t represents a test vector and x s represents a target vector.
14. The method according to claim 8 , further including:
calculating a first likelihood representing that one of two view components is same among view components of at least two non-discrete views in two multi-view vectors and a second likelihood representing that the one of the two view components is different among the view components of the at least two non-discrete views in the two multi-view vectors based on the determined parameters of the multi-view vector model, and determining whether the one of the two view components is the same in the two multi-view vectors based on the first and second likelihoods, where
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where x t represents a test vecto, x s represents a target vector, and calculation for v j is symmetrical to the calclation for u i .
15. The method according to claim 1 , where the multi-view vector is obtained by directly vectorizing the object to be represented.
16. The method according to claim 1 , where the classifier is trained by:
simultaneously marking features of multiple non-discrete views of training samples, with a discreteness between unmarked views and marked views is higher than a discreteness between the marked views; and
training the classifier with the marked training sample.
17. A multi-view vector processing device, including a processor and a non-transitory storage medium storing program codes, which when being executed by the processor, cause the processor to control:
establishing a model of the multi-view vector x, where the model includes at least components of: a population mean μ of the multi-view vector x, a view component of a view among the at least two non-discrete views of the multi-view vector x and noise ; and
using training data of the multi-view vector x to obtain the population mean μ, parameters of the view component and parameters of the noise ,
where,
the multi-view vector is obtained by processing a feature vector with a classifier, and the feature vector is obtained by directly vectorizing the object, and
the classifier relatively separates the multi-view vector from the feature vector obtained by directly vectorizinq the object to be represented, and a discreteness between an excluded view and the two non-discrete views of the multi-view vector x is higher than a discreteness between the two non-discrete views of the multi-view vector x.
18. A non-transitory computer readable storage medium, storing program codes, where the program codes implements a method when being executed by a processor, by:
establishing a model of the multi-view vector x, where the model includes at least components of: a population mean μ of the multi-view vector x, a view component of a view among the at least two non-discrete views of the multi-view vector x and noise ; and
using training data of the multi-view vector x to obtain the population mean μ, parameters of the view component and parameters of the noise ,
where,
the multi-view vector is obtained by processing a feature vector with a classifier, and the feature vector is obtained by directly vectorizinq the object, and
the classifier relatively separates the multi-view vector from the feature vector obtained by directly vectorizinq the object to be represented, and a discreteness between an excluded view and the two non-discrete views of the multi-view vector x is higher than a discreteness between the two non-discrete views of the multi-view vector x.