System and method for machine learning architecture for partially-observed multimodal data

A method for training a machine learning model architecture for conducting machine learning using partially-observed data by using a variational selective auto-encoder (VSAE) machine learning model framework, the method comprising:

• receiving one or more data sets representative of the partially-observed data, each having a subset of observed data and a subset of unobserved data, the data receiver configured to extract a mask data structure from each data set of the one or more data sets representative of which modalities are observed and which modalities are unobserved;
• and
• maintaining a attributive proposal network for processing the one or more data sets, the attributive proposal network including a set of individual encoders, each individual encoder adapted for a corresponding observed modality;
• maintaining a collective proposal network for processing the corresponding mask data structure, the collective proposal network including a collective encoder corresponding to all of the unobserved modalities, the mask data structure utilized for conditional selection of a proposal distribution for an unobserved modality;
• and
• maintaining a first generative network including a first set of one or more decoders, each decoder of the first set of the one or more decoders configured to generate output estimated data proposed by the attributive proposal network and the collective proposal network wherein, for the unobserved modalities, expectation over collective observation from the collective proposal network is applied as a corresponding proposal distribution as an approximation of a true posterior distribution based on the mask data structure such that a joint distribution of all attributes and mask data structure can be learned from the partially-observed data.

A non-transitory computer readable medium storing machine interpretable data structures representing a machine learning model architecture, the machine learning model architecture trained using a method for conducting machine learning using partially-observed data by using a variational selective auto-encoder (VSAE) machine learning model framework, the method comprising:

• receiving one or more data sets representative of the partially-observed data, each having a subset of observed data and a subset of unobserved data, the data receiver configured to extract a mask data structure from each data set of the one or more data sets representative of which modalities are observed and which modalities are unobserved;
• and
• maintaining a attributive proposal network for processing the one or more data sets, the attributive proposal network including a set of individual encoders, each individual encoder adapted for a corresponding observed modality;
• maintaining a collective proposal network for processing the corresponding mask data structure, the collective proposal network including a collective encoder corresponding to all of the unobserved modalities, the mask data structure utilized for conditional selection of a proposal distribution for an unobserved modality;
• and
• maintaining a first generative network including a first set of one or more decoders, each decoder of the first set of the one or more decoders configured to generate output estimated data proposed by the attributive proposal network and the collective proposal network wherein, for the unobserved modalities, expectation over collective observation from the collective proposal network is applied as a corresponding proposal distribution as an approximation of a true posterior distribution based on the mask data structure such that a joint distribution of all attributes and mask data structure can be learned from the partially-observed data.