Therefore, we propose a novel deep multi-view fuzzy K-means with weight allocation and entropy regularization (DMFKM) algorithm for deep multi-view clustering. DMFKM flexibly integrates cross-view information by employing learnable view weights and utilizes a common membership matrix and centroid ...
Deep multi-view fuzzy k-means with weight allocation and entropy regularization Multi-view clustering is a rapidly evolving research topic that exploits cross-view data obtained from different domains or modalities to describe the targ... Y Li,X Xie - Applied Intelligence: The International Journal...
Minimum entropy regularizers have been used in other contexts to encode learnability priors. Input-Dependent Regularization When the model is regularized (e.g. with weight decay), the conditional entropy is prevented from being too small close to the decision surface. This will favor putting the d...
{s}\right)$ towards a few actions or action sequences, since it is easier for the actor and critic to overoptimise to a small portion of the environment. To reduce this problem, entropy regularization adds an entropy term to the loss to promote action diversity:$$H(X) = -\sum\pi\...
For example, in TRPO variant [17], entropy regularization is added to the surrogate objective. Similarly, Ma [14] integrated entropy into reward, and log probability into state value function and state–action value functions, to improve both TRPO and PPO. In this study, we refer this kind ...
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)# Instantiate the trainer objectlearner = momentum_sgd(frcn_output.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight) trainer = Trainer(frcn_output, (ce, pe), learner)# Get minibatches of images...
Validity of Fuzzy Clustering Using Entropy Regularization We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which allows us to find automatically the actual number of cl... H Sahbi,N Boujemaa - IEEE 被引量: 18发表: 2005年 Efficient subspace clusteri...
regularization—proportion of performance attributed to weight/bias values 0(default) |numeric value in the range (0,1) Proportion of performance attributed to weight/bias values, specified as a double between 0 (the default) and 1. A larger value penalizes the network for large weights, and ...
A common form of regularization is to maximize policyentropy to avoid premature convergence and lead to more stochastic policies forexploration through action space. However, this does not ensure exploration in thestate space. In this work, we instead consider the distribution of discounted weight-...
Maximum entropy, as applied to image restoration, can be thought of as a particular case of a more general technique known as regularization. One approach to ill-posed problems, such as image restoration, is to find solutions that are consistent with the data, but which possess other desirable...