For speech recognition, it is necessary to maximize the inter-phoneme variation while minimizing the intra-phoneme variation in the feature space.
For speech recognition, it is necessary to maximize the inter-phoneme variation while minimizing the intra-phoneme variation in the feature space, whereas for speaker recognition, the focus is on speaker variation instead of phoneme variation.
Concretely, RDA characterizes the intra-class reconstruction scatter as well as the inter-class reconstruction scatter, seeking to find the projections that simultaneously maximize the inter-class reconstruction scatter and minimize the intra-class reconstruction scatter.
In the representation space of nonlinear transforms in the hidden layers, a distance metric learning is explicitly designed to minimize the pair-wise intra-class variation and maximize the inter-class variation.
We believe the cause of this weak performance was that, as mentioned in Section 4.3, we could only set the number of impostor pairs at nine against a genuine pair, and hence, RankSVM could not effectively maximize the inter-subject variation.
To achieve a better clustering than agglomerative hierarchical clustering and existing graph partitioning formulations, our proposed objective function seeks to minimize the intra-cluster distances and at the same time it seeks to maximize the inter-cluster distances.
To that end, LDA tries to maximize the inter-class scatter matrix and minimize the intra-class scatter matrix simultaneously.
This is also equivalent to maximizing the inter-class variance.
The optimum threshold is computed such that the intra-class variance between the two classes of foreground and background pixels is minimal, which also corresponds to maximizing the inter-class variance between the two classes of the pixels.
It selects a threshold using the histogram of a grayscale image to find the image's optimal threshold, which maximizes the inter-class variance to obtain a larger separation between the foreground and background.
The SSWC is given by the average of S x i, 1≤i≤N, i.e.: mathrm{SWC} = frac{1}{N}S_{_{i=1}^{N}S_{x_{i}} (3) and the higher the SWC value, the better the performance of the clustering algorithm, since maximizing SSWC means minimizing the intra-cluster distance at,i while maximizing the inter-cluster distance b t i.
