# Non-Negative Kernel Sparse Coding for the Classification of Motion Data

## TL;DR

This paper introduces a novel non-negative kernel sparse coding method that combines dynamic time warping and sparse coding to effectively decompose and classify motion data, improving interpretability and discrimination.

## Contribution

It extends sparse coding with kernelization for similarity data and enforces non-negativity, enabling meaningful motion data decomposition and classification.

## Key findings

- Effective motion data decomposition demonstrated on benchmarks.
- Enhanced interpretability and discrimination in motion classification.
- Kernelized sparse coding outperforms traditional methods.

## Abstract

We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meaningful dictionary. Empirical evaluations on motion capture benchmarks show the effectiveness of our framework regarding interpretation and discrimination concerns.