Abstract: Various embodiments of systems and methods for cross media joint friend and item recommendations are disclosed herein.

Abstract: Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.

Abstract: Various embodiments of systems and methods for cross media joint friend and item recommendations are disclosed herein.

Abstract: Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.

Abstract: Various embodiments of systems and methods for cross media joint friend and item recommendations are disclosed herein.

Abstract: An audio input method includes: in an audio-input mode, receiving a first audio input by a user, recognizing the first audio to generate a first recognition result, and displaying corresponding verbal content to the user based on the first recognition result; and in an editing mode, receiving a second audio input by the user and recognizing and generating a second recognition result, converting the second recognition result to an editing instruction, and executing a corresponding operation based on the editing operation. The audio-input mode and the editing mode are switchable.

Abstract: An audio input method includes: in an audio-input mode, receiving a first audio input by a user, recognizing the first audio to generate a first recognition result, and displaying corresponding verbal content to the user based on the first recognition result; and in an editing mode, receiving a second audio input by the user and recognizing and generating a second recognition result, converting the second recognition result to an editing instruction, and executing a corresponding operation based on the editing operation. The audio-input mode and the editing mode are switchable.

Abstract: Systems and methods for executing an unsupervised feature selection algorithm on a processor which directly embeds feature selection into a clustering algorithm using sparse learning are disclosed. The direct embedding of the feature selection, via sparse learning, reduces storage requirement of collected data. In one method, unsupervised feature selection may be accomplished through a removal of redundant, irrelevant, and/or noisy features of incoming high-dimensional data.