This paper proposes a technique for jointly quantizing continuous features and the posterior distributions of their class labels based on minimizing empirical information loss, such that the index K of the quantizer region to which a given feature X is assigned approximates a sufficient statistic for its class label Y . We derive an alternating minimization procedure for learning a finite set o...

The most facial emotion recognition methods use the geometrical features of face such as eye opening and mouth opening, which extraction of them is a hard and complicated task. But, in this paper, we propose to use the discriminant features simply extracted by nonparametric weighted feature extraction (NWFE). Moreover, to model the inherent uncertainties contained in the emotional features, we ...

Single tuition fee or called UKT is the amount of determined based on student's economic ability. In its application, there are still many students who object to group that obtained. Therefore, university must apply right and accurate method in group. This study aims obtain result student’s classification using K-Nearest Neighbor (KNN) algorithm with Euclidean Distance calculation determine acc...

This paper proposes a new gait recognition method using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is first applied to 1D time-varying distance signals derived from a sequence of silhouette images to reduce it’s dimensionality. Then, LDA is performed to optimize the pattern class ificovtion..And,Spatiotemporal Correlation (STC) and Normalized Euclidean Distan...

This work 1 studies music classification using the 1-Nearest Neighbor rule comparing the Euclidean distance metric to an information distance metric based on Kolmogorov Complexity. The reason for this comparison is two-fold. First, to understand the music classification task and how similarity measures play a role. Secondly, to contribute to the knowledge regarding effective similarity measures...

With the increasing availability of terrain data, e.g., from aerial laser scans, the management of such data is attracting increasing attention in both industry and academia. In particular, spatial queries, e.g., k-nearest neighbor and reverse nearest neighbor queries, in Euclidean and spatial network spaces are being extended to terrains. Such queries all rely on an important operation, that o...

In this paper, we present two novel class-based weighting methods for the Euclidean nearest neighbor algorithm and compare them with global weighting methods considering empirical results on a widely accepted time series classification benchmark dataset. Our methods provide higher accuracy than every global weighting in nearly half of the cases and they have better overall performance. We concl...

We describe a fast method for dimensionality reduction and feature selection of ratio features for classification in hyperspectral data. The case study chosen is to discriminate internally damaged almond nuts from normal ones. For this case study, we find that using the ratios of the responses in several wavebands provides better features than a subset of waveband responses. We find that use of...

This paper describe five different principled ways to embed songs into a Euclidean metric space. In particular, we learn embeddings so that the pairwise Euclidean distance between two songs reflects semantic dissimilarity. This allows distance-based analysis, such as for example straightforward nearest-neighbor classification, to detect and potentially suggest similar songs within a collection....