Sentence examples for dimensional sparse from inspiring English sources

Additionally, a very large number of words can be identified with many being infrequent or unique to a single document leading to a high dimensional, sparse feature space.

Although most graph-based semi-supervised dimensionality reduction approaches perform very well on clean data sets, they usually cannot construct a faithful graph which plays an important role in getting a good performance, when performing on the high dimensional, sparse or noisy data.

In fact, it can also be shown that if initial weights are derived from regular lasso estimates, the adaptive lasso penalty is also consistent for variable selection in high dimensional sparse settings (Shojaie and Michailidis, 2010b).

Data symbols of users are mapped to multi-dimensional sparse codewords.

However, it generates high-dimensional sparse data matrix with a high feature-to-instance ratio.

First, we considered one-dimensional sparse vectors x N×1, where N is the length of the sparse vector.

By compressing the original high-dimensional sparse BoI vectors into compact binary descriptors, over 100 × speed up can be achieved with sacrificing 0.05 precision at 0.30 recall.

Furthermore, numerical experiments were conducted on one-dimensional sparse signals and two-dimensional compressible signals, where the two-dimensional signals were the coefficients from wavelet transforms.

This algorithm converts spatial vectors of high-dimensional, sparse short texts into new, lower-dimensional, substantive feature spaces by using deep learning network.

While such priors are widely used for estimating high-dimensional sparse vectors, selecting a subset of variables remains a daunting task.

In earlier work, we presented a one-dimensional cache-oblivious sparse matrix vector (SpMV) multiplication scheme which has its roots in one-dimensional sparse matrix partitioning.