You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The second major contribution of our work is to propose a new method forhandling multimodal data in the context of feature learning.The use ofRGB-D data, as opposed to simple 2D image data, has been shown tosignificantly improve grasp detection results [28, 14, 56].In this work, we present a multimodal feature learning algorithm which adds astructured regularization penalty to the objective function to be optimizedduring learning.As opposed to previous works in deeplearning, which either ignore modality information at the first layer (i.e., encourage all features to use all modalities) [59] ortrain separate first-layer features for each modality [43, 61], ourapproach allows for a middle-ground in which each feature is encouraged touse only a subset of the input modalities, but is not forced to use onlyparticular ones. To solve these problems, we propose a new algorithm for feature learning formultimodal data.Our approach incorporates astructured penalty term into the optimization problem to be solved during learning. This techniqueallows the model to learn correlated features between multiple input modalities,but regularizes the number of modalities used per feature (hidden unit),discouraging themodel from learning weak correlations between modalities.With this regularization term, the algorithm can specify how mode-sparse or mode-dense the features should be, representing a continuum between the two extremes outlined above.

Question: Why is the paper's proposed multimodal feature learning algorithm better than other methods that "ignore modality information at the first layer" or "train separate first-layer features for each modality"?
The proposed approach incorporates a structured penalty term into the optimization problem to be solved during learning. This technique allows the model to learn correlated features between multiple input modalities,but regularizes the number of modalities used per feature (hidden unit),discouraging the model from learning weak correlations between modalities.With this regularization term, the algorithm can specify how mode-sparse or mode-dense the features should be, representing a continuum between the two extremes outlined above.  The second major contribution of this work is to propose a new method for handling multimodal data in the context of feature learning.The use of RGB-D data, as opposed to simple 2D image data, has been shown to significantly improve grasp detection results. In this work, we present a multimodal feature learning algorithm which adds a structured regularization penalty to the objective function to be optimized during learning.  ignore modality information at the first layer (i.e., encourage all features to use all modalities) or train separate first-layer features for each modality.