### Table 1. Comparison of training and prediction times on a high dimensional problem

2005

Cited by 2

### Table 2: Rate of correct dimensionality estimation for high dimensional data

### Table 4 Rate of correct dimensionality estimation for high dimensional data

### Table 6.3: FVU values for high-dimensional example S / N PLS-1 PLS-2 PLS-3 3-Layer NN NLPLS

1997

Cited by 13

### Table 9: Average hop count and flit traversal energy under SPLASH benchmark traces.

2005

"... In PAGE 6: ... Then we apply the hop count values numerically to a variety of topologies to find the optimal topology. Table9 lists (Havg, Eflit) values under these traffic patterns. We do not include high-dimensional tori due to limitations of RSIM, but it suffices for demonstration.... ..."

Cited by 12

### Table 2: A sample data set illustrates clusters embedded in subspaces of a high dimensional space.

2003

"... In PAGE 2: ... Hence, a good subspace clustering algorithm should be able to find clusters and the maximum associated set of dimensions. Consider, for example, a data set with 5 data points of 6 dimensional(given in Table2 ). In this data set, it is obvious that C = {x1, x2, x3} is a cluster and the maximum set of dimensions should be P = {1, 2, 3, 4}.... In PAGE 3: ...here sj is a vector defined as sj = (Aj1, Aj2, ..., Ajnj)T. Since there are possibly multiple states(or values) for a vari- able, a symbol table of a data set is usually not unique. For example, for the data set in Table2 , Table 3 is one of its symbol tables. BC BS A A A A B B B B C C C C D D D D BD BT Table 3: One of the symbol tables of the data set in Table 2.... In PAGE 3: ... For a given symbol table of the data set, the frequency table of each cluster is unique according to that symbol table. For example, for the data set in Table2 , let (C, P) be a subspace cluster, where C = {x1, x2, x3} and P = {1, 2, 3, 4}, if we use the symbol table presented in Table 3, then the corre- sponding frequency table for the subspace cluster (C, P) is given in Table 4. From the definition of frequency fjr in Equation (6), we have the following equalities: nj CG r=1 fjr(C) = |C|, j = 1, 2, .... ..."

Cited by 4

### TABLE II ACCURACY OF DIAGNOSTIC CLASSIFICATION: HIGH DIMENSIONAL BIOLOGICAL BENCHMARK DATASETS (% CORRECT IN LOOCV STUDY)

2003

Cited by 2

### Table 4.3: High dimensional stifi ODE system I: classical approach.

2005

### Table III. Eigenvalues of covariance of high dimensional remotely sensed data. Eigenvalue Proportion Accumulation

1993

### Table 3 and 4 show similar trends to those for high-dimensional tori. Linear load, larger buffer size and technology progress all make networks more likely to benefit from topology improvements, but technology progress also makes networks more sensitive to workload.

2005

"... In PAGE 4: ... In order for a hierar- chical torus to save energy, the following inequality must hold ER9 a29 N 2v a4 v 2 a31 a26 ER5 a6 N 2 a21 Na3 vER5 a18 ER9a5 a16 v2ER9 which is equivalent to v a16 ER9 ER5 (4) N a16 v2ER9 vER5 a18 ER9 (5) Inequality (4) determines the minimal express interval for a hier- archical torus to achieve better energy efficiency than a 2-D torus, and inequality (5) determines the minimal network size for a certain express interval. Table3 lists the minimal express interval and corre- sponding minimal network size in the form of a3 va25 Nmina5 . Table 3: Minimal express interval and corresponding minimal network size for hierarchical tori, in the form of a3 va25 Nmina5 .... In PAGE 4: ... Table 3 lists the minimal express interval and corre- sponding minimal network size in the form of a3 va25 Nmina5 . Table3 : Minimal express interval and corresponding minimal network size for hierarchical tori, in the form of a3 va25 Nmina5 . Linear load Constant load buffer size 4-flit 16-flit 4-flit 16-flit 0.... In PAGE 5: ... For hierarchical tori and express cubes, ER9 ER5 is also the minimal express interval. From Table3 and 5, the minimal express interval switches between 2 and 3 at 35nm technology for different load mod- els, so which topology is better depends on which load model is closer to reality. 4.... ..."

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