该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5／3小波变换，消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS（Hi...该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5／3小波变换，消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS（Hierarchical Alternating Least Squares）算法进行非负分解，来消除光谱冗余和空间残余冗余，同时保护了光谱信息。最后，将分解的因子矩阵进行熵编码。实验结果表明，该文提出的压缩算法具有良好压缩性能，在压缩比32：1-4：1范围内，平均信噪比高于40dB，与传统高光谱图像压缩算法比较，平均峰值信噪比提高了1．499dB。有效地提高了高光谱图像压缩算法的压缩性能和保护了光谱信息。展开更多
The nonnegative tensor （matrix） factorization finds more and more applications in various disciplines including machine learning, data mining, and blind source separation, etc. In computation, the optimization probl...The nonnegative tensor （matrix） factorization finds more and more applications in various disciplines including machine learning, data mining, and blind source separation, etc. In computation, the optimization problem involved is solved by alternatively minimizing one factor while the others are fixed. To solve the subproblem efficiently, we first exploit a variable regularization term which makes the subproblem far from ill-condition. Second, an augmented Lagrangian alternating direction method is employed to solve this convex and well-conditioned regularized subproblem, and two accelerating skills are also implemented. Some preliminary numerical experiments are performed to show the improvements of the new method.展开更多
How to extract robust feature is an important research topic in machine learning community. In this paper, we investigate robust feature extraction for speech signal based on tensor structure and develop a new method ...How to extract robust feature is an important research topic in machine learning community. In this paper, we investigate robust feature extraction for speech signal based on tensor structure and develop a new method called constrained Nonnegative Tensor Factorization （cNTF）. A novel feature extraction framework based on the cortical representation in primary auditory cortex （A1） is proposed for robust speaker recognition. Motivated by the neural firing rates model in A1, the speech signal first is represented as a general higher order tensor, cNTF is used to learn the basis functions from multiple interrelated feature subspaces and find a robust sparse representation for speech signal. Computer simulations are given to evaluate the performance of our method and comparisons with existing speaker recognition methods are also provided. The experimental results demonstrate that the proposed method achieves higher recognition accuracy in noisy environment.展开更多
文摘该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5／3小波变换，消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS（Hierarchical Alternating Least Squares）算法进行非负分解，来消除光谱冗余和空间残余冗余，同时保护了光谱信息。最后，将分解的因子矩阵进行熵编码。实验结果表明，该文提出的压缩算法具有良好压缩性能，在压缩比32：1-4：1范围内，平均信噪比高于40dB，与传统高光谱图像压缩算法比较，平均峰值信噪比提高了1．499dB。有效地提高了高光谱图像压缩算法的压缩性能和保护了光谱信息。
文摘The nonnegative tensor （matrix） factorization finds more and more applications in various disciplines including machine learning, data mining, and blind source separation, etc. In computation, the optimization problem involved is solved by alternatively minimizing one factor while the others are fixed. To solve the subproblem efficiently, we first exploit a variable regularization term which makes the subproblem far from ill-condition. Second, an augmented Lagrangian alternating direction method is employed to solve this convex and well-conditioned regularized subproblem, and two accelerating skills are also implemented. Some preliminary numerical experiments are performed to show the improvements of the new method.
基金supported by the National Natural Science Foundation of China under Grant No.60775007the National Basic Research 973 Program of China under Grant No.2005CB724301the Science and Technology Commission of Shanghai Municipality under Grant No.08511501701
文摘How to extract robust feature is an important research topic in machine learning community. In this paper, we investigate robust feature extraction for speech signal based on tensor structure and develop a new method called constrained Nonnegative Tensor Factorization （cNTF）. A novel feature extraction framework based on the cortical representation in primary auditory cortex （A1） is proposed for robust speaker recognition. Motivated by the neural firing rates model in A1, the speech signal first is represented as a general higher order tensor, cNTF is used to learn the basis functions from multiple interrelated feature subspaces and find a robust sparse representation for speech signal. Computer simulations are given to evaluate the performance of our method and comparisons with existing speaker recognition methods are also provided. The experimental results demonstrate that the proposed method achieves higher recognition accuracy in noisy environment.
