A novel sparsity optimization method is proposed to select features for multi-class classification problems by directly optimizing a l2,p -norm ( 0 < p ≤ 1 ) based sparsity function subject to data-fitting inequality constraints to obtain large between-class margins. The direct sparse optimization method circumvents the empirical tuning of regularization parameters in existing feature selection...

• Proper loss function selection improves smoothness better than the reduction of data sparsity. The less sensitive a deep model is to sparsity, smoother extracted manifold is. Simply stacking hidden layers in does not significantly improve smoothness. Deep useful tool that can extract smooth from data. As computationally intensive method, however, parameters and sparsity are common factors lik...

Öneri sistemleri kullanıcıların geçmişteki tercihlerinden hareketle gelecekteki tercihlerini tahmin eden sistemlerdir. Fakat kullanıcılar her zaman sistemlere belirtmeyebilir. Bu durum, öneri tasarlanırken karşılaşılan en büyük sorunlardan biri olan veri seyrekliğine neden olur. Derin öğrenme algoritmalarından otomatik kodlayıcılar, seyrek kullanıcı matrisini verilerden öğrendiği iç görülerden ...

Enforcing sparsity constraints has been shown to be an effective and efficient way to obtain state-of-the-art results in regression and classification tasks. Unlike the support vector machine (SVM) the relevance vector machine (RVM) explicitly encodes the criterion of model sparsity as a prior over the model weights. However the lack of an explicit prior structure over the weight variances mean...