In this paper, we propose a method for suppressing a large number of interferences by using multichannel amplitude analysis based on nonnegative matrix factorization (NMF) and its effective semi-supervised training. For the point-source interference reduction of an asynchronous microphone array, we propose amplitude-based speech enhancement in the time-channel domain, which we call transfer-function-gain NMF. Transfer-function-gain NMF is a robust method against drift, which disrupts an inter-channel phase analysis. We use this method to suppress a large number of sources. We show that a mass of interferences can be modeled by a single basis assuming that the noise sources are sufficiently far from the microphones and the spatial characteristics become similar to each other. Since the blind optimization of the NMF parameters does not work well with merely sparse observation contaminated by the constant heavy noise, we train the diffuse noise basis in advance of the noise suppression using a speech absent observation, which can be obtained easily using a simple voice activity detection technique. We confirmed the effectiveness of our proposed model and semi-supervised transfer-function-gain NMF in an experiment simulating a target source that was surrounded by a diffuse noise.