Time Series Classification in Dissimilarity Spaces
نویسندگان
چکیده
Time series classification in the dissimilarity space combines the advantages of the dynamic time warping and the rich mathematical structure of Euclidean spaces. We applied dimension reduction using PCA followed by support vector learning on dissimilarity representations to 43 UCR datasets. Results indicate that time series classification in dissimilarity space has potential to complement the state-of-the-art.
منابع مشابه
On Using Asymmetry Information for Classification in Extended Dissimilarity Spaces
When asymmetric dissimilarity measures arise, asymmetry correction methods such as averaging are used in order to make the matrix symmetric. This is usually needed for the application of pattern recognition procedures, but in this way the asymmetry information is lost. In this paper we present a new approach to make use of the asymmetry information in dissimilarity spaces. We show that taking i...
متن کاملUltrametricity of Dissimilarity Spaces and Its Significance for Data Mining
We introduce a measure of ultrametricity for dissimilarity spaces and examine transformations of dissimilarities that impact this measure. Then, we study the influence of ultrametricity on the behavior of two classes of data mining algorithms (kNN classification and PAM clustering) applied on dissimilarity spaces. We show that there is an inverse variation between ultrametricity and performance...
متن کاملPrototype Selection for Classification in Standard and Generalized Dissimilarity Spaces
A common way to represent patterns for recognition systems is by feature vectors lying in some space. If this representation is based only on the predefined object features, it is independent of the other objects. In contrast, a dissimilarity representation of objects takes into account the relations between them by some measure of resemblance (e.g. dissimilarity). The nearest neighbour (1-NN) ...
متن کاملDissimilarity Representations Using lp-norms in Eigen Spaces
This paper presents an empirical evaluation on a dissimilarity measure strategy by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is...
متن کاملSimilarity Searches in Heterogeneous Feature Spaces
Correlating event streams or development paths of observed behavior that involves disparate types of data is a common problem in many applications including biomedical and clinical diagnosis systems. We present a new formulation of the following dual problem: (a) given multiple event streams for which we have prior knowledge, specify a feature space with heterogeneous dissimilarity measures, an...
متن کامل