sparse representation造句

例句与造句

1. However, under weak decoherence assumption, a quantum dynamical map can find a sparse representation.
2. This allows sparse representations of inputs.
3. These include representations for signals ( Fourier, wavelets, frames ), sampling theory, and sparse representations.
4. Experimentally, sparse representations of sensory information have been observed in many systems, including vision, audition, touch, and olfaction.
5. In the afternoon, a smaller meeting concluded that one problem the OSCE faces is the sparse representation of women in the parliamentary assembly.
6. It's difficult to find sparse representation in a sentence. 用sparse representation造句挺难的
7. The sparse representation term x _ i = e _ k enforces K-means algorithm to use only one atom ( column ) in dictionary D.
8. More recently, Tarokh has also contributed to orthogonal frequency division multiplexing, cognitive radio and collaborative communications, pricing, scheduling, sparse representations theory and distributed beamforming.
9. Jews had " sparse representation in European arts and sciences through the beginning of the 19C ", but within a century Jews were disproportionately represented ( except in astronomy ).
10. Independent component analysis is a technique that creates sparse representations in an automated fashion, and the semi-discrete and non-negative matrix approaches sacrifice accuracy of representation in order to reduce computational complexity.
11. This method allows us to gradually update the dictionary as new data becomes available for sparse representation learning and helps drastically reduce the amount of memory needed to store the dataset ( which often has a huge size ).
12. It also has properties that are useful for signal denoising since usually one can learn a dictionary to represent the meaningful part of the input signal in a sparse way but the noise in the input will have a much less sparse representation.
13. In particular, a minimization problem is formulated, where the objective function consists of the classification error, the representation error, an " L1 " regularization on the representing weights for each data point ( to enable sparse representation of data ), and an " L2 " regularization on the parameters of the classifier.