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author | Vasil Zlatanov <v@skozl.com> | 2019-02-12 17:40:09 +0000 |
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committer | Vasil Zlatanov <v@skozl.com> | 2019-02-12 17:40:09 +0000 |
commit | 4d92df7d253d0262eb8dbb854cd0afbfff4969f7 (patch) | |
tree | ffcf302fa6eeb60200017b46b70a2aa986cfeb05 | |
parent | 1cfebbbda1fd9fc6d3a4eb9ce9d1ba79971bcc92 (diff) | |
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Remove italic aorund K-means
-rw-r--r-- | report/paper.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/report/paper.md b/report/paper.md index 7483c2e..a75a7c9 100644 --- a/report/paper.md +++ b/report/paper.md @@ -116,7 +116,7 @@ more. This is due to the complexity added by the two-pixels test, since it adds # RF codebook -An alternative to codebook creation via *K-means* involves using an ensemble of totally random trees. We code each decriptor according to which leaf of each tree in the ensemble it is sorted. This effectively performs and unsupervised transformation of our dataset to a high-dimensional spare representation. The dimension of the vocubulary size is determined by the number of leaves in each random tree and the ensemble size. +An alternative to codebook creation via K-means involves using an ensemble of totally random trees. We code each decriptor according to which leaf of each tree in the ensemble it is sorted. This effectively performs and unsupervised transformation of our dataset to a high-dimensional spare representation. The dimension of the vocubulary size is determined by the number of leaves in each random tree and the ensemble size. \begin{figure}[H] \begin{center} |