Sentence examples for dimension of vectors from inspiring English sources

Note that although it is possible to increase the dimension of vectors to add some features, we have seen little improvement with more vectors.

In sampling scheme, K is the dimension of vectors Z[l], where there exists ( K=leftlceil frac{T+{W}_g}{W_g} Nrightrceil + N-1 ) from Eq. (12).

The numerator is the Euclidean distance in the (N) dimension of vectors (x) and (y), and the denominator is the sum of the vectors' lengths.

This algorithm can preserve the cosine similarity between vectors and shapely reduces the dimension of vectors, which in turns greatly reduces the time of calculating our feature distance similarity matrix.

Specifically, the dimension of vectors x m and x n is d(=5J×(L+1)) as defined in the previous subsection, and each extraction matrix E l extracts 5J features of each meteorological element F l.

For each pair of feature vectors r and p (representing the RNA feature vector and the protein feature vector, respectively), we want to train a matrix M and use the score < p| M| r > to measure the interaction between r and p. M will be a 100-D matrix because the dimension of vectors was set at 10.

Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm.

In the sparse sampling signal model of RBSR, the dimension of vector x is still M×1.

The dimension of vector u is 2L F (L F + 2 N t.

This is, the j-th dimension of vector P contains the number of times that the location (l_{j}) is mentioned by the tweets in T. On the other hand, the j-th dimension of vector I contains the number of tweets in T that were posted by users in the location (l_{j}).

So the form of Equation 1 is equivalent to the basic version of sparse representation problem with noise when we significant increase ω or PRI to make the dimension of vector y much less than M×1.