We determine the information-theoretic cutoff value on separation of cluster centers for exact recovery labels in a K-component Gaussian mixture model with equal sizes. Moreover, we show that semidefinite programming (SDP) relaxation K-means clustering method achieves such sharp threshold without assuming symmetry centers.

Gaussian mixture modeling is a generative probabilistic model that assumes the observed data are generated from of multiple distributions. This provides flexible approach to complex distributions may not be easily represented by single distribution. The with noise component refers finite includes an additional background or outliers in data. helps take into account presence anomalies latter asp...

In inference, open-set classification is to either classify a sample into known class from training or reject it as an unknown class. Existing deep classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability distinguish classes. contrast, our model cooperatively learn reconstruction and perform c...

We present an unsupervised classification algorithm based on an ICA mixture model. A mixture model is a model in which the observed data can be categorized into several mutually exclusive data classes. In an ICA mixture model, it is assumed that the data in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources and the mixing matrix for ...

The efficiency of many speech processing methods rely on accurate modeling of the distribution of the signal spectrum and a majority of prior works suggest that the spectral components follow the Laplace distribution. To improve the probability distribution models based on our knowledge of speech source modeling, we argue that the model should in fact be a multiplicative mixture model, includin...