Name
Abstract | ||
|---|---|---|
This paper proposes some extensions to the work on kernels dedicated to
string alignment (biological sequence alignment) based on the summing up of
scores obtained by local alignments with gaps. The extensions we propose allow
to construct, from classical time-warp distances, what we called summative
time-warp kernels that are positive definite if some simple sufficient
conditions are satisfied. Furthermore, from the same formalism, we derive a
time-warp inner product that extends the usual euclidean inner product,
providing the capability to handle discrete sequences or time series of
variable lengths in an Hilbert space. The classification experiment we
conducted, using either first near neighbor classifier or Support Vector
Machine classifier leads to conclude that the positive definite elastic kernels
we propose outperform the distance substituting kernels for the classical
elastic distances we tested. In a similar way, the kernel based on the distance
induced by the time-warp inner product outperforms significantly on the
considered task the kernel based on the euclidean distance. |
Year | Venue | DocType |
|---|---|---|
2010 | Computing Research Repository | Journal |
Volume | Citations | PageRank |
abs/1005.5 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre-François Marteau | 1 | 62 | 17.30 |
| Sylvie Gibet | 2 | 367 | 52.50 |